From 46519720eb75a1d914fbe20443305d8c4da8aa3c Mon Sep 17 00:00:00 2001 From: Nan Jiang Date: Tue, 15 Sep 2020 12:39:31 -0400 Subject: [PATCH 01/36] Update README.md --- README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index e0fb2af97..90f13cffa 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ You can download and install Bullet using the [vcpkg](https://github.com/Microso cd vcpkg ./bootstrap-vcpkg.sh ./vcpkg integrate install - vcpkg install bullet3 + ./vcpkg install bullet3 The Bullet port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please [create an issue or pull request](https://github.com/Microsoft/vcpkg) on the vcpkg repository. @@ -91,9 +91,9 @@ p.connect(p.SHARED_MEMORY) #or (p.TCP, "localhost", 6667) or (p.UDP, "192.168.86 Make sure cmake is installed (sudo apt-get install cmake, brew install cmake, or https://cmake.org) In a terminal type: - - ./build_cmake_pybullet_double.sh - +``` +./build_cmake_pybullet_double.sh +``` This script will invoke cmake and build in the build_cmake directory. You can find pybullet in Bullet/examples/pybullet. The BulletExampleBrowser binary will be in Bullet/examples/ExampleBrowser. @@ -101,15 +101,15 @@ You can also build Bullet using premake. There are premake executables in the bu Depending on your system (Linux 32bit, 64bit or Mac OSX) use one of the following lines Using premake: ``` - cd build3 - ./premake4_linux --double gmake - ./premake4_linux64 --double gmake - ./premake4_osx --double --enable_pybullet gmake +cd build3 +./premake4_linux --double gmake +./premake4_linux64 --double gmake +./premake4_osx --double --enable_pybullet gmake ``` Then ``` - cd gmake - make +cd gmake +make ``` Note that on Linux, you need to use cmake to build pybullet, since the compiler has issues of mixing shared and static libraries. @@ -117,9 +117,9 @@ Note that on Linux, you need to use cmake to build pybullet, since the compiler **Mac OSX Xcode** Click on build3/xcode4.command or in a terminal window execute - - ./premake_osx xcode4 - +``` +./premake_osx xcode4 +``` ## Usage The App_ExampleBrowser executables will be located in the bin folder. From c0d9553d6521d1528ac44fb2a0eb5edbacbb5895 Mon Sep 17 00:00:00 2001 From: yycho0108 Date: Wed, 20 Jan 2021 14:58:44 +0900 Subject: [PATCH 02/36] add link names args to CreateMultibody --- examples/SharedMemory/PhysicsClientC_API.cpp | 4 ++- examples/SharedMemory/PhysicsClientC_API.h | 3 +- .../PhysicsServerCommandProcessor.cpp | 10 ++++++ examples/SharedMemory/SharedMemoryCommands.h | 1 + .../b3RobotSimulatorClientAPI_NoDirect.cpp | 3 +- examples/pybullet/pybullet.c | 31 ++++++++++++++++--- 6 files changed, 45 insertions(+), 7 deletions(-) diff --git a/examples/SharedMemory/PhysicsClientC_API.cpp b/examples/SharedMemory/PhysicsClientC_API.cpp index 829eeca56..466eeb293 100644 --- a/examples/SharedMemory/PhysicsClientC_API.cpp +++ b/examples/SharedMemory/PhysicsClientC_API.cpp @@ -2052,7 +2052,8 @@ B3_SHARED_API int b3CreateMultiBodyLink(b3SharedMemoryCommandHandle commandHandl const double linkInertialFrameOrientation[4], int linkParentIndex, int linkJointType, - const double linkJointAxis[3]) + const double linkJointAxis[3], + const char* const linkName) { struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; b3Assert(command); @@ -2097,6 +2098,7 @@ B3_SHARED_API int b3CreateMultiBodyLink(b3SharedMemoryCommandHandle commandHandl command->m_createMultiBodyArgs.m_linkJointAxis[3 * linkIndex + 2] = linkJointAxis[2]; command->m_createMultiBodyArgs.m_linkMasses[linkIndex] = linkMass; + command->m_createMultiBodyArgs.m_linkNames[linkIndex] = linkName; command->m_createMultiBodyArgs.m_numLinks++; return numLinks; } diff --git a/examples/SharedMemory/PhysicsClientC_API.h b/examples/SharedMemory/PhysicsClientC_API.h index 318263b2d..7c7ac1442 100644 --- a/examples/SharedMemory/PhysicsClientC_API.h +++ b/examples/SharedMemory/PhysicsClientC_API.h @@ -552,7 +552,8 @@ extern "C" const double linkInertialFrameOrientation[/*4*/], int linkParentIndex, int linkJointType, - const double linkJointAxis[/*3*/]); + const double linkJointAxis[/*3*/], + const char* const linkName); //batch creation is an performance feature to create a large number of multi bodies in one command B3_SHARED_API int b3CreateMultiBodySetBatchPositions(b3PhysicsClientHandle physClient, b3SharedMemoryCommandHandle commandHandle, double* batchPositions, int numBatchObjects); diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 295e202c5..f358b5d23 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -2232,6 +2232,11 @@ struct ProgrammaticUrdfInterface : public URDFImporterInterface ///pure virtual interfaces, precondition is a valid linkIndex (you can assert/terminate if the linkIndex is out of range) virtual std::string getLinkName(int linkIndex) const { + // Allow user overrides on default-created link names. + if(m_createBodyArgs.m_linkNames[linkIndex] != nullptr + && strlen(m_createBodyArgs.m_linkNames[linkIndex]) > 0){ + return std::string(m_createBodyArgs.m_linkNames[linkIndex]); + } std::string linkName = "link"; char numstr[21]; // enough to hold all numbers up to 64-bits sprintf(numstr, "%d", linkIndex); @@ -2309,6 +2314,11 @@ struct ProgrammaticUrdfInterface : public URDFImporterInterface virtual std::string getJointName(int linkIndex) const { + // Allow user overrides on default-created joint names. + if(m_createBodyArgs.m_linkNames[linkIndex] != nullptr + && strlen(m_createBodyArgs.m_linkNames[linkIndex]) > 0){ + return std::string(m_createBodyArgs.m_linkNames[linkIndex]); + } std::string jointName = "joint"; char numstr[21]; // enough to hold all numbers up to 64-bits sprintf(numstr, "%d", linkIndex); diff --git a/examples/SharedMemory/SharedMemoryCommands.h b/examples/SharedMemory/SharedMemoryCommands.h index a019a58ca..311379d79 100644 --- a/examples/SharedMemory/SharedMemoryCommands.h +++ b/examples/SharedMemory/SharedMemoryCommands.h @@ -1057,6 +1057,7 @@ struct b3CreateMultiBodyArgs int m_flags; int m_numBatchObjects; + const char* m_linkNames[MAX_CREATE_MULTI_BODY_LINKS]; }; struct b3CreateMultiBodyResultArgs diff --git a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp index f2777e129..4eb95c4b3 100644 --- a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp +++ b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp @@ -2431,7 +2431,8 @@ int b3RobotSimulatorClientAPI_NoDirect::createMultiBody(struct b3RobotSimulatorC doubleLinkInertialFrameOrientation, linkParentIndex, linkJointType, - doubleLinkJointAxis); + doubleLinkJointAxis, + 0); } statusHandle = b3SubmitClientCommandAndWaitStatus(sm, command); diff --git a/examples/pybullet/pybullet.c b/examples/pybullet/pybullet.c index 56b5f7511..b6387f3c6 100644 --- a/examples/pybullet/pybullet.c +++ b/examples/pybullet/pybullet.c @@ -128,6 +128,24 @@ static int pybullet_internalGetIntFromSequence(PyObject* seq, int index) return v; } +static const char* pybullet_internalGetCStringFromSequence(PyObject* seq, int index) +{ + const char* v = 0; + PyObject* item; + + if (PyList_Check(seq)) + { + item = PyList_GET_ITEM(seq, index); + v = PyUnicode_AsUTF8(item); + } + else + { + item = PyTuple_GET_ITEM(seq, index); + v = PyUnicode_AsUTF8(item); + } + return v; +} + // internal function to set a float matrix[16] // used to initialize camera position with // a view and projection matrix in renderImage() @@ -9350,18 +9368,19 @@ static PyObject* pybullet_createMultiBody(PyObject* self, PyObject* args, PyObje PyObject* linkInertialFramePositionObj = 0; PyObject* linkInertialFrameOrientationObj = 0; PyObject* objBatchPositions = 0; + PyObject* linkNamesObj = 0; static char* kwlist[] = { "baseMass", "baseCollisionShapeIndex", "baseVisualShapeIndex", "basePosition", "baseOrientation", "baseInertialFramePosition", "baseInertialFrameOrientation", "linkMasses", "linkCollisionShapeIndices", "linkVisualShapeIndices", "linkPositions", "linkOrientations", "linkInertialFramePositions", "linkInertialFrameOrientations", "linkParentIndices", - "linkJointTypes", "linkJointAxis", "useMaximalCoordinates", "flags", "batchPositions", "physicsClientId", NULL}; + "linkJointTypes", "linkJointAxis", "useMaximalCoordinates", "flags", "batchPositions", "linkNames", "physicsClientId", NULL}; - if (!PyArg_ParseTupleAndKeywords(args, keywds, "|diiOOOOOOOOOOOOOOiiOi", kwlist, + if (!PyArg_ParseTupleAndKeywords(args, keywds, "|diiOOOOOOOOOOOOOOiiOOi", kwlist, &baseMass, &baseCollisionShapeIndex, &baseVisualShapeIndex, &basePosObj, &baseOrnObj, &baseInertialFramePositionObj, &baseInertialFrameOrientationObj, &linkMassesObj, &linkCollisionShapeIndicesObj, &linkVisualShapeIndicesObj, &linkPositionsObj, &linkOrientationsObj, &linkInertialFramePositionObj, &linkInertialFrameOrientationObj, &linkParentIndicesObj, - &linkJointTypesObj, &linkJointAxisObj, &useMaximalCoordinates, &flags, &objBatchPositions, &physicsClientId)) + &linkJointTypesObj, &linkJointAxisObj, &useMaximalCoordinates, &flags, &objBatchPositions, &linkNamesObj, &physicsClientId)) { return NULL; } @@ -9398,6 +9417,7 @@ static PyObject* pybullet_createMultiBody(PyObject* self, PyObject* args, PyObje PyObject* seqLinkInertialFrameOrientations = linkInertialFrameOrientationObj ? PySequence_Fast(linkInertialFrameOrientationObj, "expected a sequence") : 0; PyObject* seqBatchPositions = objBatchPositions ? PySequence_Fast(objBatchPositions, "expected a sequence") : 0; + PyObject* seqLinkNames = linkNamesObj ? PySequence_Fast(linkNamesObj, "expected a sequence") : 0; if ((numLinkMasses == numLinkCollisionShapes) && (numLinkMasses == numLinkVisualShapes) && @@ -9448,6 +9468,7 @@ static PyObject* pybullet_createMultiBody(PyObject* self, PyObject* args, PyObje double linkInertialFrameOrientation[4]; int linkParentIndex; int linkJointType; + const char* linkName; pybullet_internalGetVector3FromSequence(seqLinkInertialFramePositions, i, linkInertialFramePosition); pybullet_internalGetVector4FromSequence(seqLinkInertialFrameOrientations, i, linkInertialFrameOrientation); @@ -9456,6 +9477,7 @@ static PyObject* pybullet_createMultiBody(PyObject* self, PyObject* args, PyObje pybullet_internalGetVector3FromSequence(seqLinkJoinAxis, i, linkJointAxis); linkParentIndex = pybullet_internalGetIntFromSequence(seqLinkParentIndices, i); linkJointType = pybullet_internalGetIntFromSequence(seqLinkJointTypes, i); + linkName = seqLinkNames? pybullet_internalGetCStringFromSequence(seqLinkNames, i) : 0; b3CreateMultiBodyLink(commandHandle, linkMass, @@ -9467,7 +9489,8 @@ static PyObject* pybullet_createMultiBody(PyObject* self, PyObject* args, PyObje linkInertialFrameOrientation, linkParentIndex, linkJointType, - linkJointAxis); + linkJointAxis, + linkName); } if (seqLinkMasses) From 6f65a12b3aa4a811010007ceba203e71b5a7f824 Mon Sep 17 00:00:00 2001 From: yycho0108 Date: Wed, 20 Jan 2021 16:45:23 +0900 Subject: [PATCH 03/36] no nullptr --- examples/SharedMemory/PhysicsServerCommandProcessor.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index f358b5d23..8eae3eaa2 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -2233,7 +2233,7 @@ struct ProgrammaticUrdfInterface : public URDFImporterInterface virtual std::string getLinkName(int linkIndex) const { // Allow user overrides on default-created link names. - if(m_createBodyArgs.m_linkNames[linkIndex] != nullptr + if(m_createBodyArgs.m_linkNames[linkIndex] != 0 && strlen(m_createBodyArgs.m_linkNames[linkIndex]) > 0){ return std::string(m_createBodyArgs.m_linkNames[linkIndex]); } @@ -2315,7 +2315,7 @@ struct ProgrammaticUrdfInterface : public URDFImporterInterface virtual std::string getJointName(int linkIndex) const { // Allow user overrides on default-created joint names. - if(m_createBodyArgs.m_linkNames[linkIndex] != nullptr + if(m_createBodyArgs.m_linkNames[linkIndex] != 0 && strlen(m_createBodyArgs.m_linkNames[linkIndex]) > 0){ return std::string(m_createBodyArgs.m_linkNames[linkIndex]); } From f719c27b04ed754f61e3a356eaf18bb11fb9abbc Mon Sep 17 00:00:00 2001 From: "daniel.ordonez" Date: Wed, 27 Jan 2021 21:59:49 -0500 Subject: [PATCH 04/36] Fix Stable PD bug on First Order Taylor approximation of next q state --- .../pybullet/examples/pdControllerStable.py | 93 +++++++++---------- 1 file changed, 46 insertions(+), 47 deletions(-) diff --git a/examples/pybullet/examples/pdControllerStable.py b/examples/pybullet/examples/pdControllerStable.py index 250a4f56b..bec3356c4 100644 --- a/examples/pybullet/examples/pdControllerStable.py +++ b/examples/pybullet/examples/pdControllerStable.py @@ -70,43 +70,34 @@ class PDControllerStableMultiDof(object): Kp = np.diagflat(kps) Kd = np.diagflat(kds) - p = Kp.dot(qError) + # Compute -Kp(q + qdot - qdes) + p_term = Kp.dot(qError - qdot*timeStep) + # Compute -Kd(qdot - qdotdes) + d_term = Kd.dot(qdoterr) - #np.savetxt("pb_qError.csv", qError, delimiter=",") - #np.savetxt("pb_p.csv", p, delimiter=",") - - d = Kd.dot(qdoterr) - - #np.savetxt("pb_d.csv", d, delimiter=",") - #np.savetxt("pbqdoterr.csv", qdoterr, delimiter=",") - - M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1, flags=1) - - M2 = np.array(M1) - #np.savetxt("M2.csv", M2, delimiter=",") - - M = (M2 + Kd * timeStep) - - #np.savetxt("pbM_tKd.csv",M, delimiter=",") - - c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1) - - c = np.array(c1) - #np.savetxt("pbC.csv",c, delimiter=",") - A = M - #p = [0]*43 - b = p + d - c - #np.savetxt("pb_acc.csv",b, delimiter=",") - qddot = np.linalg.solve(A, b) - tau = p + d - Kd.dot(qddot) * timeStep - #print("len(tau)=",len(tau)) + # Compute Inertia matrix M(q) + M = self._pb.calculateMassMatrix(bodyUniqueId, q1, flags=1) + M = np.array(M) + # Given: M(q) * qddot + C(q, qdot) = T_ext + T_int + # Compute Coriolis and External (Gravitational) terms G = C - T_ext + G = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1) + G = np.array(G) + # Obtain estimated generalized accelerations, considering Coriolis and Gravitational forces, and stable PD actions + qddot = np.linalg.solve(a=(M + Kd * timeStep), + b=p_term + d_term - G) + # Compute control generalized forces (T_int) + tau = p_term + d_term - Kd.dot(qddot) * timeStep + # Clip generalized forces to actuator limits maxF = np.array(maxForces) - forces = np.clip(tau, -maxF, maxF) - return forces + generalized_forces = np.clip(tau, -maxF, maxF) + return generalized_forces class PDControllerStable(object): - + """ + Implementation based on: Tan, J., Liu, K., & Turk, G. (2011). "Stable proportional-derivative controllers" + DOI: 10.1109/MCG.2011.30 + """ def __init__(self, pb): self._pb = pb @@ -121,28 +112,36 @@ class PDControllerStable(object): q1.append(jointStates[i][0]) qdot1.append(jointStates[i][1]) zeroAccelerations.append(0) + q = np.array(q1) qdot = np.array(qdot1) qdes = np.array(desiredPositions) qdotdes = np.array(desiredVelocities) + qError = qdes - q qdotError = qdotdes - qdot + Kp = np.diagflat(kps) Kd = np.diagflat(kds) - p = Kp.dot(qError) - d = Kd.dot(qdotError) - forces = p + d - M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1) - M2 = np.array(M1) - M = (M2 + Kd * timeStep) - c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations) - c = np.array(c1) - A = M - b = -c + p + d - qddot = np.linalg.solve(A, b) - tau = p + d - Kd.dot(qddot) * timeStep + # Compute -Kp(q + qdot - qdes) + p_term = Kp.dot(qError - qdot*timeStep) + # Compute -Kd(qdot - qdotdes) + d_term = Kd.dot(qdotError) + + # Compute Inertia matrix M(q) + M = self._pb.calculateMassMatrix(bodyUniqueId, q1) + M = np.array(M) + # Given: M(q) * qddot + C(q, qdot) = T_ext + T_int + # Compute Coriolis and External (Gravitational) terms G = C - T_ext + G = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations) + G = np.array(G) + # Obtain estimated generalized accelerations, considering Coriolis and Gravitational forces, and stable PD actions + qddot = np.linalg.solve(a=(M + Kd * timeStep), + b=(-G + p_term + d_term)) + # Compute control generalized forces (T_int) + tau = p_term + d_term - (Kd.dot(qddot) * timeStep) + # Clip generalized forces to actuator limits maxF = np.array(maxForces) - forces = np.clip(tau, -maxF, maxF) - #print("c=",c) - return tau + generalized_forces = np.clip(tau, -maxF, maxF) + return generalized_forces From 97add36b9fda8d76396ff84a9cf1567cfb508b30 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Thu, 25 Feb 2021 16:32:39 -0800 Subject: [PATCH 05/36] update bullet_client.py to accept options p.connect(p.GUI, options="--background_color_red=1 --background_color_blue=1 --background_color_green=1") If you use bullet_client.py, you can use: p = bc.BulletClient(connection_mode=pybullet.GUI,options="--background_color_red=1 --background_color_blue=1 --background_color_green=1") --mouse_move_multiplier=0.4 --mouse_wheel_multiplier=0.01 --mp4=moviename.mp4 --- examples/pybullet/gym/pybullet_utils/bullet_client.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/pybullet/gym/pybullet_utils/bullet_client.py b/examples/pybullet/gym/pybullet_utils/bullet_client.py index f4995d3b5..6543313d0 100644 --- a/examples/pybullet/gym/pybullet_utils/bullet_client.py +++ b/examples/pybullet/gym/pybullet_utils/bullet_client.py @@ -9,7 +9,7 @@ import pybullet class BulletClient(object): """A wrapper for pybullet to manage different clients.""" - def __init__(self, connection_mode=None, hostName=None): + def __init__(self, connection_mode=None, hostName=None, options=''): """Creates a Bullet client and connects to a simulation. Args: @@ -22,15 +22,15 @@ class BulletClient(object): """ self._shapes = {} if connection_mode is None: - self._client = pybullet.connect(pybullet.SHARED_MEMORY) + self._client = pybullet.connect(pybullet.SHARED_MEMORY, options=options) if self._client >= 0: return else: connection_mode = pybullet.DIRECT if hostName is None: - self._client = pybullet.connect(connection_mode) + self._client = pybullet.connect(connection_mode, options=options) else: - self._client = pybullet.connect(connection_mode, hostName=hostName) + self._client = pybullet.connect(connection_mode, hostName=hostName, options=options) def __del__(self): """Clean up connection if not already done.""" From 782f14caa15bcdd803655ff743647075ff7ef231 Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Sun, 28 Feb 2021 15:15:07 +0000 Subject: [PATCH 06/36] heightfield processAllTriangles: Copy 1 less vertex The 2 triangles of a heightfield quad share 2 vertices. Co-authored-by: Andrew Shulaev --- .../btHeightfieldTerrainShape.cpp | 23 +++++++++++-------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp index cab6980b6..8aafc0505 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp @@ -349,27 +349,32 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback if (m_flipQuadEdges || (m_useDiamondSubdivision && !((j + x) & 1)) || (m_useZigzagSubdivision && !(j & 1))) { - //first triangle getVertex(x, j, vertices[indices[0]]); getVertex(x, j + 1, vertices[indices[1]]); getVertex(x + 1, j + 1, vertices[indices[2]]); callback->processTriangle(vertices, 2 * x, j); - //second triangle - // getVertex(x,j,vertices[0]);//already got this vertex before, thanks to Danny Chapman - getVertex(x + 1, j + 1, vertices[indices[1]]); + + // already set: getVertex(x, j, vertices[indices[0]]) + + // equivalent to: getVertex(x + 1, j + 1, vertices[indices[1]]); + vertices[indices[1]] = vertices[indices[2]]; + getVertex(x + 1, j, vertices[indices[2]]); + callback->processTriangle(vertices, 2 * x+1, j); } else { - //first triangle getVertex(x, j, vertices[indices[0]]); getVertex(x, j + 1, vertices[indices[1]]); getVertex(x + 1, j, vertices[indices[2]]); callback->processTriangle(vertices, 2 * x, j); - //second triangle - getVertex(x + 1, j, vertices[indices[0]]); - //getVertex(x,j+1,vertices[1]); + + // already set: getVertex(x, j + 1, vertices[indices[1]]); + + // equivalent to: getVertex(x + 1, j, vertices[indices[0]]); + vertices[indices[0]] = vertices[indices[2]]; + getVertex(x + 1, j + 1, vertices[indices[2]]); callback->processTriangle(vertices, 2 * x+1, j); } @@ -846,4 +851,4 @@ void btHeightfieldTerrainShape::buildAccelerator(int chunkSize) void btHeightfieldTerrainShape::clearAccelerator() { m_vboundsGrid.clear(); -} \ No newline at end of file +} From e7e28bebf8eb639702ea4315b3aeb730a7356e9f Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Sun, 28 Feb 2021 15:39:36 +0000 Subject: [PATCH 07/36] heightfield processAllTriangles: Skip triangle processing if the triangle is out-of-AABB Skips triangle processing if the triangle is out-of-AABB on the up axis. This massively improves OpenMW performance on my test machine. Co-authored-by: Andrew Shulaev --- .../btHeightfieldTerrainShape.cpp | 29 +++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp index 8aafc0505..3acfa79b7 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp @@ -352,7 +352,13 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback getVertex(x, j, vertices[indices[0]]); getVertex(x, j + 1, vertices[indices[1]]); getVertex(x + 1, j + 1, vertices[indices[2]]); - callback->processTriangle(vertices, 2 * x, j); + + // Skip triangle processing if the triangle is out-of-AABB. + btScalar minUp = btMin(vertices[0][m_upAxis], btMin(vertices[1][m_upAxis], vertices[2][m_upAxis])); + btScalar maxUp = btMax(vertices[0][m_upAxis], btMax(vertices[1][m_upAxis], vertices[2][m_upAxis])); + + if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j, vertices[indices[0]]) @@ -360,15 +366,24 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback vertices[indices[1]] = vertices[indices[2]]; getVertex(x + 1, j, vertices[indices[2]]); - - callback->processTriangle(vertices, 2 * x+1, j); + minUp = btMin(minUp, vertices[indices[2]][m_upAxis]); + maxUp = btMax(maxUp, vertices[indices[2]][m_upAxis]); + + if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + callback->processTriangle(vertices, 2 * x+1, j); } else { getVertex(x, j, vertices[indices[0]]); getVertex(x, j + 1, vertices[indices[1]]); getVertex(x + 1, j, vertices[indices[2]]); - callback->processTriangle(vertices, 2 * x, j); + + // Skip triangle processing if the triangle is out-of-AABB. + btScalar minUp = btMin(vertices[0][m_upAxis], btMin(vertices[1][m_upAxis], vertices[2][m_upAxis])); + btScalar maxUp = btMax(vertices[0][m_upAxis], btMax(vertices[1][m_upAxis], vertices[2][m_upAxis])); + + if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j + 1, vertices[indices[1]]); @@ -376,7 +391,11 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback vertices[indices[0]] = vertices[indices[2]]; getVertex(x + 1, j + 1, vertices[indices[2]]); - callback->processTriangle(vertices, 2 * x+1, j); + minUp = btMin(minUp, vertices[indices[2]][m_upAxis]); + maxUp = btMax(maxUp, vertices[indices[2]][m_upAxis]); + + if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + callback->processTriangle(vertices, 2 * x+1, j); } } } From bb838dbec08cbf1b7c1460899dd1f8d4af606ddc Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Mon, 1 Mar 2021 09:57:08 +0000 Subject: [PATCH 08/36] heightfield up axis: Use Range Reads a bit better than using individual min/max scalars. --- .../btHeightfieldTerrainShape.cpp | 66 ++++++++++++++----- 1 file changed, 51 insertions(+), 15 deletions(-) diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp index 3acfa79b7..74a2a327b 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp @@ -232,6 +232,43 @@ getQuantized( return (int)(x + 0.5); } +static btHeightfieldTerrainShape::Range makeRange(btScalar min, btScalar max) +{ + btHeightfieldTerrainShape::Range result; + result.min = min; + result.max = max; + return result; +} + +static bool rangesHaveOverlap(const btHeightfieldTerrainShape::Range& a, const btHeightfieldTerrainShape::Range& b) +{ + return !(a.min > b.max || a.max < b.min); +} + +// Equivalent to std::minmax({a, b, c}). +// Performs at most 3 comparisons. +static btHeightfieldTerrainShape::Range minmaxRange(btScalar a, btScalar b, btScalar c) +{ + if (a > b) + { + if (b > c) + return makeRange(c, a); + else if (a > c) + return makeRange(b, a); + else + return makeRange(b, c); + } + else + { + if (a > c) + return makeRange(c, b); + else if (b > c) + return makeRange(a, b); + else + return makeRange(a, c); + } +} + /// given input vector, return quantized version /** This routine is basically determining the gridpoint indices for a given @@ -334,7 +371,8 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback } // TODO If m_vboundsGrid is available, use it to determine if we really need to process this area - + + const Range aabbUpRange = makeRange(aabbMin[m_upAxis], aabbMax[m_upAxis]); for (int j = startJ; j < endJ; j++) { for (int x = startX; x < endX; x++) @@ -354,10 +392,9 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback getVertex(x + 1, j + 1, vertices[indices[2]]); // Skip triangle processing if the triangle is out-of-AABB. - btScalar minUp = btMin(vertices[0][m_upAxis], btMin(vertices[1][m_upAxis], vertices[2][m_upAxis])); - btScalar maxUp = btMax(vertices[0][m_upAxis], btMax(vertices[1][m_upAxis], vertices[2][m_upAxis])); + Range upRange = minmaxRange(vertices[0][m_upAxis], vertices[1][m_upAxis], vertices[2][m_upAxis]); - if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + if (rangesHaveOverlap(upRange, aabbUpRange)) callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j, vertices[indices[0]]) @@ -366,11 +403,11 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback vertices[indices[1]] = vertices[indices[2]]; getVertex(x + 1, j, vertices[indices[2]]); - minUp = btMin(minUp, vertices[indices[2]][m_upAxis]); - maxUp = btMax(maxUp, vertices[indices[2]][m_upAxis]); + upRange.min = btMin(upRange.min, vertices[indices[2]][m_upAxis]); + upRange.max = btMax(upRange.max, vertices[indices[2]][m_upAxis]); - if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) - callback->processTriangle(vertices, 2 * x+1, j); + if (rangesHaveOverlap(upRange, aabbUpRange)) + callback->processTriangle(vertices, 2 * x + 1, j); } else { @@ -379,10 +416,9 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback getVertex(x + 1, j, vertices[indices[2]]); // Skip triangle processing if the triangle is out-of-AABB. - btScalar minUp = btMin(vertices[0][m_upAxis], btMin(vertices[1][m_upAxis], vertices[2][m_upAxis])); - btScalar maxUp = btMax(vertices[0][m_upAxis], btMax(vertices[1][m_upAxis], vertices[2][m_upAxis])); + Range upRange = minmaxRange(vertices[0][m_upAxis], vertices[1][m_upAxis], vertices[2][m_upAxis]); - if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) + if (rangesHaveOverlap(upRange, aabbUpRange)) callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j + 1, vertices[indices[1]]); @@ -391,11 +427,11 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback vertices[indices[0]] = vertices[indices[2]]; getVertex(x + 1, j + 1, vertices[indices[2]]); - minUp = btMin(minUp, vertices[indices[2]][m_upAxis]); - maxUp = btMax(maxUp, vertices[indices[2]][m_upAxis]); + upRange.min = btMin(upRange.min, vertices[indices[2]][m_upAxis]); + upRange.max = btMax(upRange.max, vertices[indices[2]][m_upAxis]); - if (!(minUp > aabbMax[m_upAxis] || maxUp < aabbMin[m_upAxis])) - callback->processTriangle(vertices, 2 * x+1, j); + if (rangesHaveOverlap(upRange, aabbUpRange)) + callback->processTriangle(vertices, 2 * x + 1, j); } } } From e4963f73f230cbb594c553864b2d61f6e410c236 Mon Sep 17 00:00:00 2001 From: vabr-g Date: Mon, 1 Mar 2021 21:21:11 +0100 Subject: [PATCH 09/36] Fix a null dereference in UrdfParser When the parser sees no tag inside a , it logs an error but does not return false. So in the next step, a null pointer is dereferenced. This can be tested with loading the following URDF: ``` ``` --- examples/Importers/ImportURDFDemo/UrdfParser.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/Importers/ImportURDFDemo/UrdfParser.cpp b/examples/Importers/ImportURDFDemo/UrdfParser.cpp index 3348665d5..235e6ee8c 100644 --- a/examples/Importers/ImportURDFDemo/UrdfParser.cpp +++ b/examples/Importers/ImportURDFDemo/UrdfParser.cpp @@ -1132,6 +1132,7 @@ bool UrdfParser::parseDeformable(UrdfModel& model, tinyxml2::XMLElement* config, if (!i) { logger->reportError("expected an inertial element"); + return false; } UrdfInertia inertia; if (!parseInertia(inertia, i, logger)) From adcaa75777f09ddc018a168416a0b391a2e2cf52 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 1 Mar 2021 14:03:48 -0800 Subject: [PATCH 10/36] enable v-hacd in cmake for BulletRobotics and pybullet build (use cmake -DENABLE_VHACD=OFF .. to disable) cmake: enable stable PD plugin (-DDSTATIC_LINK_SPD_PLUGIN) btMultibody, make some args const in mulMatrix --- CMakeLists.txt | 2 ++ Extras/BulletRobotics/CMakeLists.txt | 21 +++++++++++++++++++ examples/pybullet/CMakeLists.txt | 14 ++++++++----- .../Featherstone/btMultiBody.cpp | 2 +- src/BulletDynamics/Featherstone/btMultiBody.h | 2 +- 5 files changed, 34 insertions(+), 7 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7860f1552..d2a0c2556 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -30,6 +30,8 @@ OPTION(BUILD_SHARED_LIBS "Use shared libraries" OFF) OPTION(USE_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD "Use btSoftMultiBodyDynamicsWorld" ON) OPTION(USE_OPENVR "Use OpenVR for virtual reality" OFF) +OPTION(ENABLE_VHACD "Use VHACD in BulletRobotics and pybullet" ON) + OPTION(BULLET2_MULTITHREADING "Build Bullet 2 libraries with mutex locking around certain operations (required for multi-threading)" OFF) IF (BULLET2_MULTITHREADING) OPTION(BULLET2_USE_OPEN_MP_MULTITHREADING "Build Bullet 2 with support for multi-threading with OpenMP (requires a compiler with OpenMP support)" OFF) diff --git a/Extras/BulletRobotics/CMakeLists.txt b/Extras/BulletRobotics/CMakeLists.txt index 2442f6efc..641300fb1 100644 --- a/Extras/BulletRobotics/CMakeLists.txt +++ b/Extras/BulletRobotics/CMakeLists.txt @@ -7,6 +7,9 @@ INCLUDE_DIRECTORIES( ${BULLET_PHYSICS_SOURCE_DIR}/examples/ThirdPartyLibs/enet/include ${BULLET_PHYSICS_SOURCE_DIR}/examples/ThirdPartyLibs/clsocket/src ) +ADD_DEFINITIONS(-DSTATIC_LINK_SPD_PLUGIN) + + SET(BulletRobotics_INCLUDES ../../examples/CommonInterfaces/Common2dCanvasInterface.h @@ -156,8 +159,26 @@ SET(BulletRobotics_SRCS ${BulletRobotics_INCLUDES} ../../examples/MultiThreading/b3PosixThreadSupport.cpp ../../examples/MultiThreading/b3Win32ThreadSupport.cpp ../../examples/MultiThreading/b3ThreadSupportInterface.cpp + + ) +IF(ENABLE_VHACD) +ADD_DEFINITIONS(-DBT_ENABLE_VHACD) +SET(BulletRobotics_SRCS ${BulletRobotics_SRCS} + ../../Extras/VHACD/test/src/main_vhacd.cpp + ../../Extras/VHACD/src/VHACD.cpp + ../../Extras/VHACD/src/vhacdICHull.cpp + ../../Extras/VHACD/src/vhacdManifoldMesh.cpp + ../../Extras/VHACD/src/vhacdMesh.cpp + ../../Extras/VHACD/src/vhacdVolume.cpp +) +INCLUDE_DIRECTORIES( + ../../Extras/VHACD/inc + ../../Extras/VHACD/public +) +ENDIF(ENABLE_VHACD) + IF(BUILD_CLSOCKET) ADD_DEFINITIONS(-DBT_ENABLE_CLSOCKET) ENDIF(BUILD_CLSOCKET) diff --git a/examples/pybullet/CMakeLists.txt b/examples/pybullet/CMakeLists.txt index 1a7f0d5f2..5ee71645e 100644 --- a/examples/pybullet/CMakeLists.txt +++ b/examples/pybullet/CMakeLists.txt @@ -13,15 +13,19 @@ IF(BUILD_PYBULLET_NUMPY) ) ENDIF() -ADD_DEFINITIONS(-DSTATIC_LINK_SPD_PLUGIN) +ADD_DEFINITIONS(-DSTATIC_LINK_SPD_PLUGIN ) -SET(pybullet3_SRCS - pybullet.c - ) +IF(ENABLE_VHACD) +ADD_DEFINITIONS(-DBT_ENABLE_VHACD) + +INCLUDE_DIRECTORIES( + ../../Extras/VHACD/inc + ../../Extras/VHACD/public +) +ENDIF(ENABLE_VHACD) SET(pybullet_SRCS pybullet.c - ) IF(BUILD_CLSOCKET) diff --git a/src/BulletDynamics/Featherstone/btMultiBody.cpp b/src/BulletDynamics/Featherstone/btMultiBody.cpp index 7cb92fa3b..dfc4f92a3 100644 --- a/src/BulletDynamics/Featherstone/btMultiBody.cpp +++ b/src/BulletDynamics/Featherstone/btMultiBody.cpp @@ -1411,7 +1411,7 @@ void btMultiBody::solveImatrix(const btSpatialForceVector &rhs, btSpatialMotionV } } -void btMultiBody::mulMatrix(btScalar *pA, btScalar *pB, int rowsA, int colsA, int rowsB, int colsB, btScalar *pC) const +void btMultiBody::mulMatrix(const btScalar *pA, const btScalar *pB, int rowsA, int colsA, int rowsB, int colsB, btScalar *pC) const { for (int row = 0; row < rowsA; row++) { diff --git a/src/BulletDynamics/Featherstone/btMultiBody.h b/src/BulletDynamics/Featherstone/btMultiBody.h index 5a3efc941..9cf1ceb5a 100644 --- a/src/BulletDynamics/Featherstone/btMultiBody.h +++ b/src/BulletDynamics/Featherstone/btMultiBody.h @@ -745,7 +745,7 @@ private: } } - void mulMatrix(btScalar * pA, btScalar * pB, int rowsA, int colsA, int rowsB, int colsB, btScalar *pC) const; + void mulMatrix(const btScalar *pA, const btScalar *pB, int rowsA, int colsA, int rowsB, int colsB, btScalar *pC) const; private: btMultiBodyLinkCollider *m_baseCollider; //can be NULL From 390eeb111b9404f1cba5dd4eabc77592bdb78763 Mon Sep 17 00:00:00 2001 From: vabr-g Date: Tue, 2 Mar 2021 11:55:19 +0100 Subject: [PATCH 11/36] Fix stack overflow in parsing URDF files in Bullet When reading response from the Bullet server after a "load URDF" command, the client code allocated and zeroed buffers for the data chunks with a "+1" to account for the terminating zero. This was when the buffer pointer was interpreted as a `char *`, so it meant "+ 1 byte". When reading those buffers, however, the associated pointer was a `void *`, so reading `sizeof(void*)` (8 on my machine) bytes at a time. Therefore it was reading up to 7 bytes past the allocated (and zeroed) memory. The change fixes that by changing the "+ 1" to "+ sizeof(void*)". At one place it also extends the zeroing to the final buffer position (missing "+ 1" in the original). --- Extras/Serialize/BulletFileLoader/bFile.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/Extras/Serialize/BulletFileLoader/bFile.cpp b/Extras/Serialize/BulletFileLoader/bFile.cpp index da9f65f20..5c8f54fe3 100644 --- a/Extras/Serialize/BulletFileLoader/bFile.cpp +++ b/Extras/Serialize/BulletFileLoader/bFile.cpp @@ -643,8 +643,8 @@ char *bFile::readStruct(char *head, bChunkInd &dataChunk) if ((strcmp(oldType, "btShortIntIndexData") == 0)) { int allocLen = 2; - char *dataAlloc = new char[(dataChunk.nr * allocLen) + 1]; - memset(dataAlloc, 0, (dataChunk.nr * allocLen) + 1); + char *dataAlloc = new char[(dataChunk.nr * allocLen) + sizeof(void*)]; + memset(dataAlloc, 0, (dataChunk.nr * allocLen) + sizeof(void*)); short *dest = (short *)dataAlloc; const short *src = (short *)head; for (int i = 0; i < dataChunk.nr; i++) @@ -682,8 +682,8 @@ char *bFile::readStruct(char *head, bChunkInd &dataChunk) // numBlocks * length int allocLen = (curLen); - char *dataAlloc = new char[(dataChunk.nr * allocLen) + 1]; - memset(dataAlloc, 0, (dataChunk.nr * allocLen)); + char *dataAlloc = new char[(dataChunk.nr * allocLen) + sizeof(void*)]; + memset(dataAlloc, 0, (dataChunk.nr * allocLen) + sizeof(void*)); // track allocated addDataBlock(dataAlloc); @@ -719,8 +719,8 @@ char *bFile::readStruct(char *head, bChunkInd &dataChunk) #endif // } - char *dataAlloc = new char[(dataChunk.len) + 1]; - memset(dataAlloc, 0, dataChunk.len + 1); + char *dataAlloc = new char[(dataChunk.len) + sizeof(void*)]; + memset(dataAlloc, 0, dataChunk.len + sizeof(void*)); // track allocated addDataBlock(dataAlloc); From 364e98c92d6e4c15b67b6c66256810f2f98b412a Mon Sep 17 00:00:00 2001 From: Vaclav Brozek Date: Tue, 2 Mar 2021 18:59:31 +0100 Subject: [PATCH 12/36] Optimize calls to find*() for a single char. The character literal overload is more efficient. --- examples/ThirdPartyLibs/cpp_base64/base64.cpp | 2 +- examples/TinyRenderer/model.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ThirdPartyLibs/cpp_base64/base64.cpp b/examples/ThirdPartyLibs/cpp_base64/base64.cpp index 40b555c44..b0c002783 100644 --- a/examples/ThirdPartyLibs/cpp_base64/base64.cpp +++ b/examples/ThirdPartyLibs/cpp_base64/base64.cpp @@ -136,7 +136,7 @@ std::string base64_decode(std::string const& encoded_string, bool remove_linebre std::string copy(encoded_string); size_t pos=0; - while ((pos = copy.find("\n", pos)) != std::string::npos) { + while ((pos = copy.find('\n', pos)) != std::string::npos) { copy.erase(pos, 1); } diff --git a/examples/TinyRenderer/model.cpp b/examples/TinyRenderer/model.cpp index 1f5bebc1b..d0a009130 100644 --- a/examples/TinyRenderer/model.cpp +++ b/examples/TinyRenderer/model.cpp @@ -149,7 +149,7 @@ Vec3f Model::vert(int iface, int nthvert) void Model::load_texture(std::string filename, const char *suffix, TGAImage &img) { std::string texfile(filename); - size_t dot = texfile.find_last_of("."); + size_t dot = texfile.find_last_of('.'); if (dot != std::string::npos) { texfile = texfile.substr(0, dot) + std::string(suffix); From 8f5a00dd1e3b6ad4815eee2bf5c2987ce7a0b1d6 Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Wed, 3 Mar 2021 00:07:51 +0000 Subject: [PATCH 13/36] heightfield: Add range constructor and `overlaps` --- .../btHeightfieldTerrainShape.cpp | 35 ++++++------------- .../btHeightfieldTerrainShape.h | 10 +++++- 2 files changed, 20 insertions(+), 25 deletions(-) diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp index 74a2a327b..4ab9795d8 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp @@ -232,19 +232,6 @@ getQuantized( return (int)(x + 0.5); } -static btHeightfieldTerrainShape::Range makeRange(btScalar min, btScalar max) -{ - btHeightfieldTerrainShape::Range result; - result.min = min; - result.max = max; - return result; -} - -static bool rangesHaveOverlap(const btHeightfieldTerrainShape::Range& a, const btHeightfieldTerrainShape::Range& b) -{ - return !(a.min > b.max || a.max < b.min); -} - // Equivalent to std::minmax({a, b, c}). // Performs at most 3 comparisons. static btHeightfieldTerrainShape::Range minmaxRange(btScalar a, btScalar b, btScalar c) @@ -252,20 +239,20 @@ static btHeightfieldTerrainShape::Range minmaxRange(btScalar a, btScalar b, btSc if (a > b) { if (b > c) - return makeRange(c, a); + return btHeightfieldTerrainShape::Range(c, a); else if (a > c) - return makeRange(b, a); + return btHeightfieldTerrainShape::Range(b, a); else - return makeRange(b, c); + return btHeightfieldTerrainShape::Range(b, c); } else { if (a > c) - return makeRange(c, b); + return btHeightfieldTerrainShape::Range(c, b); else if (b > c) - return makeRange(a, b); + return btHeightfieldTerrainShape::Range(a, b); else - return makeRange(a, c); + return btHeightfieldTerrainShape::Range(a, c); } } @@ -372,7 +359,7 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback // TODO If m_vboundsGrid is available, use it to determine if we really need to process this area - const Range aabbUpRange = makeRange(aabbMin[m_upAxis], aabbMax[m_upAxis]); + const Range aabbUpRange(aabbMin[m_upAxis], aabbMax[m_upAxis]); for (int j = startJ; j < endJ; j++) { for (int x = startX; x < endX; x++) @@ -394,7 +381,7 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback // Skip triangle processing if the triangle is out-of-AABB. Range upRange = minmaxRange(vertices[0][m_upAxis], vertices[1][m_upAxis], vertices[2][m_upAxis]); - if (rangesHaveOverlap(upRange, aabbUpRange)) + if (upRange.overlaps(aabbUpRange)) callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j, vertices[indices[0]]) @@ -406,7 +393,7 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback upRange.min = btMin(upRange.min, vertices[indices[2]][m_upAxis]); upRange.max = btMax(upRange.max, vertices[indices[2]][m_upAxis]); - if (rangesHaveOverlap(upRange, aabbUpRange)) + if (upRange.overlaps(aabbUpRange)) callback->processTriangle(vertices, 2 * x + 1, j); } else @@ -418,7 +405,7 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback // Skip triangle processing if the triangle is out-of-AABB. Range upRange = minmaxRange(vertices[0][m_upAxis], vertices[1][m_upAxis], vertices[2][m_upAxis]); - if (rangesHaveOverlap(upRange, aabbUpRange)) + if (upRange.overlaps(aabbUpRange)) callback->processTriangle(vertices, 2 * x, j); // already set: getVertex(x, j + 1, vertices[indices[1]]); @@ -430,7 +417,7 @@ void btHeightfieldTerrainShape::processAllTriangles(btTriangleCallback* callback upRange.min = btMin(upRange.min, vertices[indices[2]][m_upAxis]); upRange.max = btMax(upRange.max, vertices[indices[2]][m_upAxis]); - if (rangesHaveOverlap(upRange, aabbUpRange)) + if (upRange.overlaps(aabbUpRange)) callback->processTriangle(vertices, 2 * x + 1, j); } } diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h index 2cf3c0072..987ea324f 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h @@ -75,6 +75,14 @@ btHeightfieldTerrainShape : public btConcaveShape public: struct Range { + Range() {} + Range(btScalar min, btScalar max) : min(min), max(max) {} + + bool overlaps(const Range& other) const + { + return !(min > other.max || max < other.min); + } + btScalar min; btScalar max; }; @@ -218,4 +226,4 @@ public: } }; -#endif //BT_HEIGHTFIELD_TERRAIN_SHAPE_H \ No newline at end of file +#endif //BT_HEIGHTFIELD_TERRAIN_SHAPE_H From 40a9584ccbe36b61e7ec7a4809e6f9ef5707ce51 Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Wed, 3 Mar 2021 00:38:21 +0000 Subject: [PATCH 14/36] heightfield: Add a test for processAllTriangles up-axis filtering --- test/collision/CMakeLists.txt | 3 +++ test/collision/main.cpp | 47 +++++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+) diff --git a/test/collision/CMakeLists.txt b/test/collision/CMakeLists.txt index b36c151be..ba6c783b4 100644 --- a/test/collision/CMakeLists.txt +++ b/test/collision/CMakeLists.txt @@ -24,10 +24,13 @@ ENDIF() ../../src/BulletCollision/CollisionShapes/btSphereShape.cpp ../../src/BulletCollision/CollisionShapes/btMultiSphereShape.cpp ../../src/BulletCollision/CollisionShapes/btPolyhedralConvexShape.cpp + ../../src/BulletCollision/CollisionShapes/btConcaveShape.cpp ../../src/BulletCollision/CollisionShapes/btConvexShape.cpp ../../src/BulletCollision/CollisionShapes/btConvexInternalShape.cpp ../../src/BulletCollision/CollisionShapes/btCollisionShape.cpp ../../src/BulletCollision/CollisionShapes/btConvexPolyhedron.cpp + ../../src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp + ../../src/BulletCollision/CollisionShapes/btTriangleCallback.cpp ) ADD_TEST(Test_Collision_PASS Test_Collision) diff --git a/test/collision/main.cpp b/test/collision/main.cpp index 8f85873ff..5be7a91f7 100644 --- a/test/collision/main.cpp +++ b/test/collision/main.cpp @@ -20,9 +20,12 @@ subject to the following restrictions: ///Todo: the test needs proper coverage and using a convex hull point cloud ///Also the GJK, EPA and MPR should be improved, both quality and performance +#include + #include #include "SphereSphereCollision.h" +#include "BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h" #include "BulletCollision/CollisionShapes/btSphereShape.h" #include "BulletCollision/CollisionShapes/btMultiSphereShape.h" @@ -30,6 +33,8 @@ subject to the following restrictions: #include "BulletCollision/NarrowPhaseCollision/btGjkEpa3.h" #include "BulletCollision/NarrowPhaseCollision/btMprPenetration.h" +namespace { + btVector3 MyBulletShapeSupportFunc(const void* shapeAptr, const btVector3& dir, bool includeMargin) { btConvexShape* shape = (btConvexShape*)shapeAptr; @@ -249,6 +254,48 @@ TEST(BulletCollisionTest, AnalyticSphereSphereDistance) testSphereSphereDistance(SSTM_ANALYTIC, 0.00001); } +class TriangleCollector : public btTriangleCallback +{ +public: + std::vector *triangles; + + explicit TriangleCollector(std::vector* triangles) : triangles(triangles) {} + virtual ~TriangleCollector() {} + + virtual void processTriangle(btVector3* triangle, int partId, int triangleIndex) + { + triangles->push_back(*triangle); + } +}; + +TEST(BulletCollisionTest, Heightfield_ProcessAllTriangles_FiltersByUpAxis) +{ + // A flat 2x2 heightfield. + const btScalar heightFieldData[] = { + 10.0, 10.0, + 10.0, 10.0, + }; + btHeightfieldTerrainShape shape( + /*heightStickWidth=*/2, /*heightStickLength=*/2, + &heightFieldData[0], /*heightScale=*/1, + /*minHeight=*/-10.0, /*maxHeight=*/10.0, + /*upAxis=*/2, PHY_FLOAT, /*flipQuadEdges=*/false); + + std::vector triangles; + TriangleCollector collector(&triangles); + + // AABB overlaps with the heightfield on upAxis. + shape.processAllTriangles(&collector, btVector3(0, 0, 0), btVector3(20, 20, 20)); + EXPECT_EQ(triangles.size(), 2); + + // AABB does not overlap with the heightfield on upAxis. + triangles.clear(); + shape.processAllTriangles(&collector, btVector3(0, 0, 0), btVector3(20, 20, 5)); + EXPECT_EQ(triangles.size(), 0); +} + +} // namespace + int main(int argc, char** argv) { #if _MSC_VER From 89c76981fcb70b3788f9c0742f2ad49d7e3046b0 Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Wed, 3 Mar 2021 13:52:49 +0000 Subject: [PATCH 15/36] heightfield: Better support for float/double data Previously, the heightfield constructor always assumed `PHY_FLOAT` meant `btScalar`, contrary to documentation and other usages of `PHY_FLOAT`. Adds new constructors that distinguish between `float` and `double`. The legacy constructor is still there for backwards compatibility. This allows the client to use `float` or `double` regardless of `BT_USE_DOUBLE_PRECISION`. --- .../btHeightfieldTerrainShape.cpp | 62 +++++++++++++++++-- .../btHeightfieldTerrainShape.h | 39 +++++++++--- 2 files changed, 88 insertions(+), 13 deletions(-) diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp index cab6980b6..44ad69f0a 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.cpp @@ -17,6 +17,47 @@ subject to the following restrictions: #include "LinearMath/btTransformUtil.h" +btHeightfieldTerrainShape::btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, + const float* heightfieldData, btScalar minHeight, btScalar maxHeight, + int upAxis, bool flipQuadEdges) + : m_userValue3(0), m_triangleInfoMap(0) +{ + initialize(heightStickWidth, heightStickLength, heightfieldData, + /*heightScale=*/1, minHeight, maxHeight, upAxis, PHY_FLOAT, + flipQuadEdges); +} + +btHeightfieldTerrainShape::btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, const double* heightfieldData, + btScalar minHeight, btScalar maxHeight, int upAxis, bool flipQuadEdges) + : m_userValue3(0), m_triangleInfoMap(0) +{ + initialize(heightStickWidth, heightStickLength, heightfieldData, + /*heightScale=*/1, minHeight, maxHeight, upAxis, PHY_DOUBLE, + flipQuadEdges); +} + +btHeightfieldTerrainShape::btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, const short* heightfieldData, btScalar heightScale, + btScalar minHeight, btScalar maxHeight, int upAxis, bool flipQuadEdges) + : m_userValue3(0), m_triangleInfoMap(0) +{ + initialize(heightStickWidth, heightStickLength, heightfieldData, + heightScale, minHeight, maxHeight, upAxis, PHY_SHORT, + flipQuadEdges); +} + +btHeightfieldTerrainShape::btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, const unsigned char* heightfieldData, btScalar heightScale, + btScalar minHeight, btScalar maxHeight, int upAxis, bool flipQuadEdges) + : m_userValue3(0), m_triangleInfoMap(0) +{ + initialize(heightStickWidth, heightStickLength, heightfieldData, + heightScale, minHeight, maxHeight, upAxis, PHY_UCHAR, + flipQuadEdges); +} + btHeightfieldTerrainShape::btHeightfieldTerrainShape( int heightStickWidth, int heightStickLength, const void* heightfieldData, btScalar heightScale, btScalar minHeight, btScalar maxHeight, int upAxis, @@ -24,6 +65,10 @@ btHeightfieldTerrainShape::btHeightfieldTerrainShape( :m_userValue3(0), m_triangleInfoMap(0) { + // legacy constructor: Assumes PHY_FLOAT means btScalar. +#ifdef BT_USE_DOUBLE_PRECISION + if (hdt == PHY_FLOAT) hdt = PHY_DOUBLE; +#endif initialize(heightStickWidth, heightStickLength, heightfieldData, heightScale, minHeight, maxHeight, upAxis, hdt, flipQuadEdges); @@ -33,9 +78,12 @@ btHeightfieldTerrainShape::btHeightfieldTerrainShape(int heightStickWidth, int h : m_userValue3(0), m_triangleInfoMap(0) { - // legacy constructor: support only float or unsigned char, - // and min height is zero + // legacy constructor: support only btScalar or unsigned char data, + // and min height is zero. PHY_ScalarType hdt = (useFloatData) ? PHY_FLOAT : PHY_UCHAR; +#ifdef BT_USE_DOUBLE_PRECISION + if (hdt == PHY_FLOAT) hdt = PHY_DOUBLE; +#endif btScalar minHeight = 0.0f; // previously, height = uchar * maxHeight / 65535. @@ -59,7 +107,7 @@ void btHeightfieldTerrainShape::initialize( // btAssert(heightScale) -- do we care? Trust caller here btAssert(minHeight <= maxHeight); // && "bad min/max height"); btAssert(upAxis >= 0 && upAxis < 3); // && "bad upAxis--should be in range [0,2]"); - btAssert(hdt != PHY_UCHAR || hdt != PHY_FLOAT || hdt != PHY_SHORT); // && "Bad height data type enum"); + btAssert(hdt != PHY_UCHAR || hdt != PHY_FLOAT || hdt != PHY_DOUBLE || hdt != PHY_SHORT); // && "Bad height data type enum"); // initialize member variables m_shapeType = TERRAIN_SHAPE_PROXYTYPE; @@ -152,6 +200,12 @@ btHeightfieldTerrainShape::getRawHeightFieldValue(int x, int y) const break; } + case PHY_DOUBLE: + { + val = m_heightfieldDataDouble[(y * m_heightStickWidth) + x]; + break; + } + case PHY_UCHAR: { unsigned char heightFieldValue = m_heightfieldDataUnsignedChar[(y * m_heightStickWidth) + x]; @@ -846,4 +900,4 @@ void btHeightfieldTerrainShape::buildAccelerator(int chunkSize) void btHeightfieldTerrainShape::clearAccelerator() { m_vboundsGrid.clear(); -} \ No newline at end of file +} diff --git a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h index 2cf3c0072..d1cf43408 100644 --- a/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h +++ b/src/BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h @@ -50,17 +50,15 @@ subject to the following restrictions: The heightfield heights are determined from the data type used for the heightfieldData array. - - PHY_UCHAR: height at a point is the uchar value at the + - unsigned char: height at a point is the uchar value at the grid point, multipled by heightScale. uchar isn't recommended because of its inability to deal with negative values, and low resolution (8-bit). - - PHY_SHORT: height at a point is the short int value at that grid + - short: height at a point is the short int value at that grid point, multipled by heightScale. - - PHY_FLOAT: height at a point is the float value at that grid - point. heightScale is ignored when using the float heightfield - data type. + - float or dobule: height at a point is the value at that grid point. Whatever the caller specifies as minHeight and maxHeight will be honored. The class will not inspect the heightfield to discover the actual minimum @@ -95,7 +93,8 @@ protected: union { const unsigned char* m_heightfieldDataUnsignedChar; const short* m_heightfieldDataShort; - const btScalar* m_heightfieldDataFloat; + const float* m_heightfieldDataFloat; + const double* m_heightfieldDataDouble; const void* m_heightfieldDataUnknown; }; @@ -135,11 +134,33 @@ protected: public: BT_DECLARE_ALIGNED_ALLOCATOR(); - /// preferred constructor + /// preferred constructors + btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, + const float* heightfieldData, btScalar minHeight, btScalar maxHeight, + int upAxis, bool flipQuadEdges); + btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, + const double* heightfieldData, btScalar minHeight, btScalar maxHeight, + int upAxis, bool flipQuadEdges); + btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, + const short* heightfieldData, btScalar heightScale, btScalar minHeight, btScalar maxHeight, + int upAxis, bool flipQuadEdges); + btHeightfieldTerrainShape( + int heightStickWidth, int heightStickLength, + const unsigned char* heightfieldData, btScalar heightScale, btScalar minHeight, btScalar maxHeight, + int upAxis, bool flipQuadEdges); + + /// legacy constructor /** This constructor supports a range of heightfield data types, and allows for a non-zero minimum height value. heightScale is needed for any integer-based heightfield data types. + + This legacy constructor considers `PHY_FLOAT` to mean `btScalar`. + With `BT_USE_DOUBLE_PRECISION`, it will expect `heightfieldData` + to be double-precision. */ btHeightfieldTerrainShape(int heightStickWidth, int heightStickLength, const void* heightfieldData, btScalar heightScale, @@ -150,7 +171,7 @@ public: /// legacy constructor /** The legacy constructor assumes the heightfield has a minimum height - of zero. Only unsigned char or floats are supported. For legacy + of zero. Only unsigned char or btScalar data are supported. For legacy compatibility reasons, heightScale is calculated as maxHeight / 65535 (and is only used when useFloatData = false). */ @@ -218,4 +239,4 @@ public: } }; -#endif //BT_HEIGHTFIELD_TERRAIN_SHAPE_H \ No newline at end of file +#endif //BT_HEIGHTFIELD_TERRAIN_SHAPE_H From 3963f077fc4e36809eed33b65117315d9610e3aa Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Thu, 4 Mar 2021 09:13:22 -0800 Subject: [PATCH 16/36] Fix memory leak in EGL / GUI mode, resetSimulation with textures in GUI mode. Fixes Issue #3285 --- examples/OpenGLWindow/GLInstancingRenderer.cpp | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/examples/OpenGLWindow/GLInstancingRenderer.cpp b/examples/OpenGLWindow/GLInstancingRenderer.cpp index 3754c4a9e..81f9ec8c6 100644 --- a/examples/OpenGLWindow/GLInstancingRenderer.cpp +++ b/examples/OpenGLWindow/GLInstancingRenderer.cpp @@ -397,6 +397,15 @@ void GLInstancingRenderer::removeAllInstances() m_graphicsInstances.clear(); m_data->m_publicGraphicsInstances.exitHandles(); m_data->m_publicGraphicsInstances.initHandles(); + + for (int i=0;im_textureHandles.size();i++) + { + InternalTextureHandle& h = m_data->m_textureHandles[i]; + glDeleteTextures(1, &h.m_glTexture); + } + + m_data->m_textureHandles.clear(); + } GLInstancingRenderer::~GLInstancingRenderer() From 201f3dd7d81a694f12af8616ef9b8a844c81aae5 Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Thu, 4 Mar 2021 09:55:35 -0800 Subject: [PATCH 17/36] cmake: move stable pd files from BulletRoboticsGUI to BulletRobotics. --- Extras/BulletRobotics/CMakeLists.txt | 127 +++++++++++++----------- Extras/BulletRoboticsGUI/CMakeLists.txt | 15 --- 2 files changed, 71 insertions(+), 71 deletions(-) diff --git a/Extras/BulletRobotics/CMakeLists.txt b/Extras/BulletRobotics/CMakeLists.txt index 641300fb1..7287c1437 100644 --- a/Extras/BulletRobotics/CMakeLists.txt +++ b/Extras/BulletRobotics/CMakeLists.txt @@ -97,68 +97,83 @@ SET(BulletRobotics_INCLUDES ) SET(BulletRobotics_SRCS ${BulletRobotics_INCLUDES} - ../../examples/OpenGLWindow/SimpleCamera.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/SpAlg.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/SpAlg.h + ../../examples/SharedMemory/plugins/stablePDPlugin/Shape.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/Shape.h + ../../examples/SharedMemory/plugins/stablePDPlugin/RBDUtil.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/RBDUtil.h + ../../examples/SharedMemory/plugins/stablePDPlugin/RBDModel.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/RBDModel.h + ../../examples/SharedMemory/plugins/stablePDPlugin/MathUtil.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/MathUtil.h + ../../examples/SharedMemory/plugins/stablePDPlugin/KinTree.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/KinTree.h + ../../examples/SharedMemory/plugins/stablePDPlugin/BulletConversion.cpp + ../../examples/SharedMemory/plugins/stablePDPlugin/BulletConversion.h + + ../../examples/OpenGLWindow/SimpleCamera.cpp - ../../examples/TinyRenderer/geometry.cpp - ../../examples/TinyRenderer/model.cpp - ../../examples/TinyRenderer/tgaimage.cpp - ../../examples/TinyRenderer/our_gl.cpp - ../../examples/TinyRenderer/TinyRenderer.cpp + ../../examples/TinyRenderer/geometry.cpp + ../../examples/TinyRenderer/model.cpp + ../../examples/TinyRenderer/tgaimage.cpp + ../../examples/TinyRenderer/our_gl.cpp + ../../examples/TinyRenderer/TinyRenderer.cpp - ../../examples/SharedMemory/plugins/collisionFilterPlugin/collisionFilterPlugin.cpp - ../../examples/SharedMemory/plugins/pdControlPlugin/pdControlPlugin.cpp - ../../examples/SharedMemory/b3RobotSimulatorClientAPI_NoGUI.cpp - ../../examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp - ../../examples/SharedMemory/IKTrajectoryHelper.cpp - ../../examples/SharedMemory/plugins/tinyRendererPlugin/tinyRendererPlugin.cpp - ../../examples/SharedMemory/plugins/tinyRendererPlugin/TinyRendererVisualShapeConverter.cpp - ../../examples/SharedMemory/InProcessMemory.cpp - ../../examples/SharedMemory/PhysicsClient.cpp - ../../examples/SharedMemory/PhysicsServer.cpp - ../../examples/SharedMemory/PhysicsServerSharedMemory.cpp - ../../examples/SharedMemory/PhysicsDirect.cpp - ../../examples/SharedMemory/PhysicsDirectC_API.cpp - ../../examples/SharedMemory/PhysicsServerCommandProcessor.cpp - ../../examples/SharedMemory/b3PluginManager.cpp - ../../examples/SharedMemory/PhysicsClientSharedMemory.cpp - ../../examples/SharedMemory/PhysicsClientSharedMemory_C_API.cpp - ../../examples/SharedMemory/PhysicsClientC_API.cpp - ../../examples/SharedMemory/Win32SharedMemory.cpp - ../../examples/SharedMemory/PosixSharedMemory.cpp + ../../examples/SharedMemory/plugins/collisionFilterPlugin/collisionFilterPlugin.cpp + ../../examples/SharedMemory/plugins/pdControlPlugin/pdControlPlugin.cpp + ../../examples/SharedMemory/b3RobotSimulatorClientAPI_NoGUI.cpp + ../../examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp + ../../examples/SharedMemory/IKTrajectoryHelper.cpp + ../../examples/SharedMemory/plugins/tinyRendererPlugin/tinyRendererPlugin.cpp + ../../examples/SharedMemory/plugins/tinyRendererPlugin/TinyRendererVisualShapeConverter.cpp + ../../examples/SharedMemory/InProcessMemory.cpp + ../../examples/SharedMemory/PhysicsClient.cpp + ../../examples/SharedMemory/PhysicsServer.cpp + ../../examples/SharedMemory/PhysicsServerSharedMemory.cpp + ../../examples/SharedMemory/PhysicsDirect.cpp + ../../examples/SharedMemory/PhysicsDirectC_API.cpp + ../../examples/SharedMemory/PhysicsServerCommandProcessor.cpp + ../../examples/SharedMemory/b3PluginManager.cpp + ../../examples/SharedMemory/PhysicsClientSharedMemory.cpp + ../../examples/SharedMemory/PhysicsClientSharedMemory_C_API.cpp + ../../examples/SharedMemory/PhysicsClientC_API.cpp + ../../examples/SharedMemory/Win32SharedMemory.cpp + ../../examples/SharedMemory/PosixSharedMemory.cpp - ../../examples/Utils/b3ResourcePath.cpp - ../../examples/Utils/RobotLoggingUtil.cpp - ../../examples/Utils/b3Clock.cpp - ../../examples/Utils/b3ResourcePath.cpp - ../../examples/Utils/ChromeTraceUtil.cpp + ../../examples/Utils/b3ResourcePath.cpp + ../../examples/Utils/RobotLoggingUtil.cpp + ../../examples/Utils/b3Clock.cpp + ../../examples/Utils/b3ResourcePath.cpp + ../../examples/Utils/ChromeTraceUtil.cpp - ../../examples/ThirdPartyLibs/tinyxml2/tinyxml2.cpp - ../../examples/ThirdPartyLibs/Wavefront/tiny_obj_loader.cpp - ../../examples/ThirdPartyLibs/stb_image/stb_image.cpp - ../../examples/ThirdPartyLibs/BussIK/Jacobian.cpp - ../../examples/ThirdPartyLibs/BussIK/LinearR2.cpp - ../../examples/ThirdPartyLibs/BussIK/LinearR3.cpp - ../../examples/ThirdPartyLibs/BussIK/LinearR4.cpp - ../../examples/ThirdPartyLibs/BussIK/MatrixRmn.cpp - ../../examples/ThirdPartyLibs/BussIK/Misc.cpp - ../../examples/ThirdPartyLibs/BussIK/Node.cpp - ../../examples/ThirdPartyLibs/BussIK/Tree.cpp - ../../examples/ThirdPartyLibs/BussIK/VectorRn.cpp + ../../examples/ThirdPartyLibs/tinyxml2/tinyxml2.cpp + ../../examples/ThirdPartyLibs/Wavefront/tiny_obj_loader.cpp + ../../examples/ThirdPartyLibs/stb_image/stb_image.cpp + ../../examples/ThirdPartyLibs/BussIK/Jacobian.cpp + ../../examples/ThirdPartyLibs/BussIK/LinearR2.cpp + ../../examples/ThirdPartyLibs/BussIK/LinearR3.cpp + ../../examples/ThirdPartyLibs/BussIK/LinearR4.cpp + ../../examples/ThirdPartyLibs/BussIK/MatrixRmn.cpp + ../../examples/ThirdPartyLibs/BussIK/Misc.cpp + ../../examples/ThirdPartyLibs/BussIK/Node.cpp + ../../examples/ThirdPartyLibs/BussIK/Tree.cpp + ../../examples/ThirdPartyLibs/BussIK/VectorRn.cpp - ../../examples/Importers/ImportColladaDemo/LoadMeshFromCollada.cpp - ../../examples/Importers/ImportObjDemo/LoadMeshFromObj.cpp - ../../examples/Importers/ImportObjDemo/Wavefront2GLInstanceGraphicsShape.cpp - ../../examples/Importers/ImportMJCFDemo/BulletMJCFImporter.cpp - ../../examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp - ../../examples/Importers/ImportURDFDemo/MyMultiBodyCreator.cpp - ../../examples/Importers/ImportURDFDemo/URDF2Bullet.cpp - ../../examples/Importers/ImportURDFDemo/UrdfParser.cpp - ../../examples/Importers/ImportURDFDemo/urdfStringSplit.cpp - ../../examples/Importers/ImportMeshUtility/b3ImportMeshUtility.cpp + ../../examples/Importers/ImportColladaDemo/LoadMeshFromCollada.cpp + ../../examples/Importers/ImportObjDemo/LoadMeshFromObj.cpp + ../../examples/Importers/ImportObjDemo/Wavefront2GLInstanceGraphicsShape.cpp + ../../examples/Importers/ImportMJCFDemo/BulletMJCFImporter.cpp + ../../examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp + ../../examples/Importers/ImportURDFDemo/MyMultiBodyCreator.cpp + ../../examples/Importers/ImportURDFDemo/URDF2Bullet.cpp + ../../examples/Importers/ImportURDFDemo/UrdfParser.cpp + ../../examples/Importers/ImportURDFDemo/urdfStringSplit.cpp + ../../examples/Importers/ImportMeshUtility/b3ImportMeshUtility.cpp - ../../examples/MultiThreading/b3PosixThreadSupport.cpp - ../../examples/MultiThreading/b3Win32ThreadSupport.cpp - ../../examples/MultiThreading/b3ThreadSupportInterface.cpp + ../../examples/MultiThreading/b3PosixThreadSupport.cpp + ../../examples/MultiThreading/b3Win32ThreadSupport.cpp + ../../examples/MultiThreading/b3ThreadSupportInterface.cpp ) diff --git a/Extras/BulletRoboticsGUI/CMakeLists.txt b/Extras/BulletRoboticsGUI/CMakeLists.txt index 9926f14b6..8537b57d8 100644 --- a/Extras/BulletRoboticsGUI/CMakeLists.txt +++ b/Extras/BulletRoboticsGUI/CMakeLists.txt @@ -94,21 +94,6 @@ SET(BulletRoboticsGUI_INCLUDES ) SET(BulletRoboticsGUI_SRCS ${BulletRoboticsGUI_INCLUDES} - - ../../examples/SharedMemory/plugins/stablePDPlugin/SpAlg.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/SpAlg.h - ../../examples/SharedMemory/plugins/stablePDPlugin/Shape.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/Shape.h - ../../examples/SharedMemory/plugins/stablePDPlugin/RBDUtil.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/RBDUtil.h - ../../examples/SharedMemory/plugins/stablePDPlugin/RBDModel.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/RBDModel.h - ../../examples/SharedMemory/plugins/stablePDPlugin/MathUtil.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/MathUtil.h - ../../examples/SharedMemory/plugins/stablePDPlugin/KinTree.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/KinTree.h - ../../examples/SharedMemory/plugins/stablePDPlugin/BulletConversion.cpp - ../../examples/SharedMemory/plugins/stablePDPlugin/BulletConversion.h ../../examples/ExampleBrowser/InProcessExampleBrowser.cpp ../../examples/SharedMemory/GraphicsServerExample.cpp ../../examples/SharedMemory/GraphicsClientExample.cpp From 57c9e66ea97d21b048777e2474762dc88579d38c Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Thu, 4 Mar 2021 11:31:17 -0800 Subject: [PATCH 18/36] bump up PyBullet to 3.09 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 602244f25..97447c9ec 100644 --- a/setup.py +++ b/setup.py @@ -501,7 +501,7 @@ if 'BT_USE_EGL' in EGL_CXX_FLAGS: setup( name='pybullet', - version='3.0.8', + version='3.0.9', description= 'Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning', long_description= From 237843b93a9ed34050df45f9c751084661abc8e1 Mon Sep 17 00:00:00 2001 From: mbennice Date: Fri, 5 Mar 2021 09:52:10 -0800 Subject: [PATCH 19/36] Default user data text to empty string when null. --- examples/Importers/ImportURDFDemo/UrdfParser.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/Importers/ImportURDFDemo/UrdfParser.cpp b/examples/Importers/ImportURDFDemo/UrdfParser.cpp index 235e6ee8c..672b44835 100644 --- a/examples/Importers/ImportURDFDemo/UrdfParser.cpp +++ b/examples/Importers/ImportURDFDemo/UrdfParser.cpp @@ -94,7 +94,8 @@ static void ParseUserData(const XMLElement* element, btHashMapreportError("User data tag must have a key attribute."); } - user_data.insert(key_attr, user_data_xml->GetText()); + const char* text = user_data_xml->GetText(); + user_data.insert(key_attr, text ? text : ""); } } } From 296cd85f607acdd9fbece2106d66a62bab3c7240 Mon Sep 17 00:00:00 2001 From: mbennice Date: Fri, 5 Mar 2021 09:54:53 -0800 Subject: [PATCH 20/36] Fix tab/spaces --- examples/Importers/ImportURDFDemo/UrdfParser.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/Importers/ImportURDFDemo/UrdfParser.cpp b/examples/Importers/ImportURDFDemo/UrdfParser.cpp index 672b44835..23f36a5e6 100644 --- a/examples/Importers/ImportURDFDemo/UrdfParser.cpp +++ b/examples/Importers/ImportURDFDemo/UrdfParser.cpp @@ -95,7 +95,7 @@ static void ParseUserData(const XMLElement* element, btHashMapreportError("User data tag must have a key attribute."); } const char* text = user_data_xml->GetText(); - user_data.insert(key_attr, text ? text : ""); + user_data.insert(key_attr, text ? text : ""); } } } From 5864901a2e2ece8276a036b099184c65402c1ab5 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 8 Mar 2021 09:32:43 -0800 Subject: [PATCH 21/36] bump up pybullet to 3.1.0, wheels are using numpy 1.19.5 (to be colab compatible) --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 97447c9ec..077f697c7 100644 --- a/setup.py +++ b/setup.py @@ -501,7 +501,7 @@ if 'BT_USE_EGL' in EGL_CXX_FLAGS: setup( name='pybullet', - version='3.0.9', + version='3.1.0', description= 'Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning', long_description= From 5cf8ee3360e2c9e87fd791378fd72e268f967f1f Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Mon, 15 Mar 2021 22:44:55 -0700 Subject: [PATCH 22/36] Allow to compile pybullet using btDiscreteDynamicsWorld (no multibodies and no deformables), this allows to create Jacobian and Mass matrix (and A=J*M-1*J_transpose) with MLCP solvers Add examples/pybullet/gym/pybullet_utils/readwriteurdf.py, this allows to read a URDF and write the URDF with more reasonable inertia tensors (based on mass and collision volumes) --- .../DeformableSelfCollision.cpp | 4 +- .../GwenGUISupport/GwenParameterInterface.cpp | 10 +- .../Importers/ImportURDFDemo/URDF2Bullet.cpp | 26 ++++- .../Importers/ImportURDFDemo/URDF2Bullet.h | 14 ++- .../SharedMemory/PhysicsClientExample.cpp | 8 +- .../PhysicsServerCommandProcessor.cpp | 104 ++++++++++++++++-- .../gym/pybullet_utils/readwriteurdf.py | 25 +++++ .../pybullet/gym/pybullet_utils/urdfEditor.py | 1 + .../MLCPSolvers/btMLCPSolver.cpp | 4 +- .../btDeformableMousePickingForce.h | 2 +- 10 files changed, 177 insertions(+), 21 deletions(-) create mode 100644 examples/pybullet/gym/pybullet_utils/readwriteurdf.py diff --git a/examples/DeformableDemo/DeformableSelfCollision.cpp b/examples/DeformableDemo/DeformableSelfCollision.cpp index 99180ea0c..45f19bd40 100644 --- a/examples/DeformableDemo/DeformableSelfCollision.cpp +++ b/examples/DeformableDemo/DeformableSelfCollision.cpp @@ -55,7 +55,7 @@ public: m_dynamicsWorld->stepSimulation(deltaTime, 4, internalTimeStep); } - void addCloth(btVector3 origin); + void addCloth(const btVector3& origin); virtual void renderScene() { @@ -126,7 +126,7 @@ void DeformableSelfCollision::initPhysics() getDeformableDynamicsWorld()->setLineSearch(false); m_guiHelper->autogenerateGraphicsObjects(m_dynamicsWorld); } -void DeformableSelfCollision::addCloth(btVector3 origin) +void DeformableSelfCollision::addCloth(const btVector3& origin) // create a piece of cloth { const btScalar s = 0.6; diff --git a/examples/ExampleBrowser/GwenGUISupport/GwenParameterInterface.cpp b/examples/ExampleBrowser/GwenGUISupport/GwenParameterInterface.cpp index b3b6923f0..d6fd7e0b4 100644 --- a/examples/ExampleBrowser/GwenGUISupport/GwenParameterInterface.cpp +++ b/examples/ExampleBrowser/GwenGUISupport/GwenParameterInterface.cpp @@ -1,7 +1,11 @@ #include "GwenParameterInterface.h" #include "gwenInternalData.h" #include - +#ifdef _WIN32 +#define safe_printf _snprintf +#else +#define safe_printf snprintf +#endif struct MyButtonEventHandler : public Gwen::Event::Handler { Gwen::Controls::Button* m_buttonControl; @@ -85,7 +89,7 @@ struct MySliderEventHandler : public Gwen::Event::Handler if (m_showValue) { char txt[1024]; - snprintf(txt, sizeof(txt), "%s : %.3f", m_variableName, val); + safe_printf(txt, sizeof(txt), "%s : %.3f", m_variableName, val); m_label->SetText(txt); } } @@ -231,7 +235,7 @@ void GwenParameterInterface::registerSliderFloatParameter(SliderParams& params) } pSlider->SetValue(*params.m_paramValuePointer); //dimensions[i] ); char labelName[1024]; - snprintf(labelName, sizeof(labelName), "%s", params.m_name); //axisNames[0]); + safe_printf(labelName, sizeof(labelName), "%s", params.m_name); //axisNames[0]); MySliderEventHandler* handler = new MySliderEventHandler(labelName, label, pSlider, params.m_paramValuePointer, params.m_callback, params.m_userPointer); handler->m_showValue = params.m_showValues; m_paramInternalData->m_sliderEventHandlers.push_back(handler); diff --git a/examples/Importers/ImportURDFDemo/URDF2Bullet.cpp b/examples/Importers/ImportURDFDemo/URDF2Bullet.cpp index c21e7fc9a..907ed55be 100644 --- a/examples/Importers/ImportURDFDemo/URDF2Bullet.cpp +++ b/examples/Importers/ImportURDFDemo/URDF2Bullet.cpp @@ -199,7 +199,15 @@ btScalar tmpUrdfScaling = 2; btTransform ConvertURDF2BulletInternal( const URDFImporterInterface& u2b, MultiBodyCreationInterface& creation, URDF2BulletCachedData& cache, int urdfLinkIndex, - const btTransform& parentTransformInWorldSpace, btMultiBodyDynamicsWorld* world1, + const btTransform& parentTransformInWorldSpace, + +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btDiscreteDynamicsWorld* world1, +#else + btMultiBodyDynamicsWorld* world1, +#endif + + bool createMultiBody, const char* pathPrefix, int flags, UrdfVisualShapeCache* cachedLinkGraphicsShapesIn, UrdfVisualShapeCache* cachedLinkGraphicsShapesOut, bool recursive) { @@ -505,6 +513,7 @@ btTransform ConvertURDF2BulletInternal( creation.addLinkMapping(urdfLinkIndex, mbLinkIndex); if (createMultiBody) { +#ifndef USE_DISCRETE_DYNAMICS_WORLD cache.m_bulletMultiBody->setupRevolute(mbLinkIndex, mass, localInertiaDiagonal, mbParentIndex, parentRotToThis, quatRotate(offsetInB.getRotation(), jointAxisInJointSpace), @@ -519,8 +528,10 @@ btTransform ConvertURDF2BulletInternal( btMultiBodyConstraint* con = new btMultiBodyJointLimitConstraint(cache.m_bulletMultiBody, mbLinkIndex, jointLowerLimit, jointUpperLimit); world1->addMultiBodyConstraint(con); } +#endif } else + { btGeneric6DofSpring2Constraint* dof6 = 0; if (jointType == URDFRevoluteJoint && jointLowerLimit <= jointUpperLimit) @@ -560,6 +571,7 @@ btTransform ConvertURDF2BulletInternal( if (createMultiBody) { +#ifndef USE_DISCRETE_DYNAMICS_WORLD cache.m_bulletMultiBody->setupPrismatic(mbLinkIndex, mass, localInertiaDiagonal, mbParentIndex, parentRotToThis, quatRotate(offsetInB.getRotation(), jointAxisInJointSpace), offsetInA.getOrigin(), //parent2joint.getOrigin(), -offsetInB.getOrigin(), @@ -574,6 +586,7 @@ btTransform ConvertURDF2BulletInternal( world1->addMultiBodyConstraint(con); } //printf("joint lower limit=%d, upper limit = %f\n", jointLowerLimit, jointUpperLimit); +#endif } else { @@ -775,10 +788,18 @@ bool MyIntCompareFunc(childParentIndex a, childParentIndex b) } + + + void ConvertURDF2Bullet( const URDFImporterInterface& u2b, MultiBodyCreationInterface& creation, const btTransform& rootTransformInWorldSpace, + +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btDiscreteDynamicsWorld* world1, +#else btMultiBodyDynamicsWorld* world1, +#endif bool createMultiBody, const char* pathPrefix, int flags, UrdfVisualShapeCache* cachedLinkGraphicsShapes) { URDF2BulletCachedData cache; @@ -829,7 +850,7 @@ void ConvertURDF2Bullet( { *cachedLinkGraphicsShapes = cachedLinkGraphicsShapesOut; } - +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (world1 && cache.m_bulletMultiBody) { B3_PROFILE("Post process"); @@ -855,4 +876,5 @@ void ConvertURDF2Bullet( world1->addMultiBody(mb); } + #endif } diff --git a/examples/Importers/ImportURDFDemo/URDF2Bullet.h b/examples/Importers/ImportURDFDemo/URDF2Bullet.h index 233091c1e..5fe290e86 100644 --- a/examples/Importers/ImportURDFDemo/URDF2Bullet.h +++ b/examples/Importers/ImportURDFDemo/URDF2Bullet.h @@ -8,6 +8,7 @@ class btVector3; class btTransform; class btMultiBodyDynamicsWorld; +class btDiscreteDynamicsWorld; class btTransform; class URDFImporterInterface; @@ -20,7 +21,18 @@ struct UrdfVisualShapeCache btAlignedObjectArray m_cachedUrdfLinkColors; btAlignedObjectArray m_cachedUrdfLinkVisualShapeIndices; }; +//#define USE_DISCRETE_DYNAMICS_WORLD +#ifdef USE_DISCRETE_DYNAMICS_WORLD + void ConvertURDF2Bullet(const URDFImporterInterface& u2b, + MultiBodyCreationInterface& creationCallback, + const btTransform& rootTransformInWorldSpace, + btDiscreteDynamicsWorld* world, + bool createMultiBody, + const char* pathPrefix, + int flags = 0, + UrdfVisualShapeCache* cachedLinkGraphicsShapes = 0); +#else void ConvertURDF2Bullet(const URDFImporterInterface& u2b, MultiBodyCreationInterface& creationCallback, const btTransform& rootTransformInWorldSpace, @@ -29,5 +41,5 @@ void ConvertURDF2Bullet(const URDFImporterInterface& u2b, const char* pathPrefix, int flags = 0, UrdfVisualShapeCache* cachedLinkGraphicsShapes = 0); - +#endif #endif //_URDF2BULLET_H diff --git a/examples/SharedMemory/PhysicsClientExample.cpp b/examples/SharedMemory/PhysicsClientExample.cpp index a55729eaf..491ac7a74 100644 --- a/examples/SharedMemory/PhysicsClientExample.cpp +++ b/examples/SharedMemory/PhysicsClientExample.cpp @@ -13,6 +13,12 @@ #include "PhysicsDirectC_API.h" #include "PhysicsClientC_API.h" #include "PhysicsServerSharedMemory.h" +#include +#ifdef _WIN32 +#define safe_printf _snprintf +#else +#define safe_printf snprintf +#endif struct MyMotorInfo2 { btScalar m_velTarget; @@ -655,7 +661,7 @@ void PhysicsClientExample::createButtons() if (m_numMotors < MAX_NUM_MOTORS) { char motorName[1026]; - snprintf(motorName, sizeof(motorName), "%s q", info.m_jointName); + safe_printf(motorName, sizeof(motorName), "%s q", info.m_jointName); // MyMotorInfo2* motorInfo = &m_motorTargetVelocities[m_numMotors]; MyMotorInfo2* motorInfo = &m_motorTargetPositions[m_numMotors]; motorInfo->m_velTarget = 0.f; diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 295e202c5..684fd555b 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -12,10 +12,15 @@ #include "../Importers/ImportMeshUtility/b3ImportMeshUtility.h" #include "BulletDynamics/MLCPSolvers/btDantzigSolver.h" #include "BulletDynamics/MLCPSolvers/btSolveProjectedGaussSeidel.h" +#include "BulletDynamics/MLCPSolvers/btMLCPSolver.h" + #include "BulletDynamics/Featherstone/btMultiBodyMLCPConstraintSolver.h" #include "BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.h" #include "BulletDynamics/Featherstone/btMultiBodyJointLimitConstraint.h" +//#define USE_DISCRETE_DYNAMICS_WORLD +//#define SKIP_DEFORMABLE_BODY +//#define SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD #include "../Utils/b3BulletDefaultFileIO.h" #include "BulletCollision/CollisionShapes/btHeightfieldTerrainShape.h" @@ -1626,7 +1631,11 @@ struct PhysicsServerCommandProcessorInternalData b3AlignedObjectArray m_saveWorldBodyData; +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btAlignedObjectArray m_worldImporters; +#else btAlignedObjectArray m_worldImporters; +#endif btAlignedObjectArray m_strings; @@ -1641,8 +1650,12 @@ struct PhysicsServerCommandProcessorInternalData btHashedOverlappingPairCache* m_pairCache; btBroadphaseInterface* m_broadphase; btCollisionDispatcher* m_dispatcher; - + +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btSequentialImpulseConstraintSolver* m_solver; +#else btMultiBodyConstraintSolver* m_solver; +#endif btDefaultCollisionConfiguration* m_collisionConfiguration; @@ -1654,7 +1667,12 @@ struct PhysicsServerCommandProcessorInternalData btAlignedObjectArray m_lf; #endif +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btDiscreteDynamicsWorld* m_dynamicsWorld; +#else btMultiBodyDynamicsWorld* m_dynamicsWorld; +#endif + int m_constraintSolverType; SharedMemoryDebugDrawer* m_remoteDebugDrawer; @@ -2714,18 +2732,30 @@ void PhysicsServerCommandProcessor::createEmptyDynamicsWorld(int flags) if ((0 == m_data->m_dynamicsWorld) && (0 == (flags & RESET_USE_DISCRETE_DYNAMICS_WORLD))) { - m_data->m_solver = new btMultiBodyConstraintSolver; + #ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD m_data->m_dynamicsWorld = new btSoftMultiBodyDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); #else +#ifdef USE_DISCRETE_DYNAMICS_WORLD + m_data->m_solver = new btSequentialImpulseConstraintSolver; + m_data->m_dynamicsWorld = new btDiscreteDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); +#else + m_data->m_solver = new btMultiBodyConstraintSolver; m_data->m_dynamicsWorld = new btMultiBodyDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); + #endif #endif } if (0 == m_data->m_dynamicsWorld) { +#ifdef USE_DISCRETE_DYNAMICS_WORLD + m_data->m_solver = new btSequentialImpulseConstraintSolver; + m_data->m_dynamicsWorld = new btDiscreteDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); +#else + m_data->m_solver = new btMultiBodyConstraintSolver; m_data->m_dynamicsWorld = new btMultiBodyDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); +#endif } //Workaround: in a VR application, where we avoid synchronizing between GFX/Physics threads, we don't want to resize this array, so pre-allocate it @@ -2860,13 +2890,14 @@ void PhysicsServerCommandProcessor::deleteDynamicsWorld() constraints.push_back(constraint); m_data->m_dynamicsWorld->removeConstraint(constraint); } +#ifndef USE_DISCRETE_DYNAMICS_WORLD for (i = m_data->m_dynamicsWorld->getNumMultiBodyConstraints() - 1; i >= 0; i--) { btMultiBodyConstraint* mbconstraint = m_data->m_dynamicsWorld->getMultiBodyConstraint(i); mbconstraints.push_back(mbconstraint); m_data->m_dynamicsWorld->removeMultiBodyConstraint(mbconstraint); } - +#endif for (i = m_data->m_dynamicsWorld->getNumCollisionObjects() - 1; i >= 0; i--) { btCollisionObject* obj = m_data->m_dynamicsWorld->getCollisionObjectArray()[i]; @@ -2878,12 +2909,14 @@ void PhysicsServerCommandProcessor::deleteDynamicsWorld() m_data->m_dynamicsWorld->removeCollisionObject(obj); delete obj; } +#ifndef USE_DISCRETE_DYNAMICS_WORLD for (i = m_data->m_dynamicsWorld->getNumMultibodies() - 1; i >= 0; i--) { btMultiBody* mb = m_data->m_dynamicsWorld->getMultiBody(i); m_data->m_dynamicsWorld->removeMultiBody(mb); delete mb; } +#endif #ifndef SKIP_DEFORMABLE_BODY for (int j = 0; j < m_data->m_lf.size(); j++) { @@ -3052,14 +3085,18 @@ void PhysicsServerCommandProcessor::createJointMotors(btMultiBody* mb) //motor->setRhsClamp(gRhsClamp); //motor->setMaxAppliedImpulse(0); mb->getLink(mbLinkIndex).m_userPtr = motor; +#ifndef USE_DISCRETE_DYNAMICS_WORLD m_data->m_dynamicsWorld->addMultiBodyConstraint(motor); +#endif motor->finalizeMultiDof(); } if (mb->getLink(mbLinkIndex).m_jointType == btMultibodyLink::eSpherical) { btMultiBodySphericalJointMotor* motor = new btMultiBodySphericalJointMotor(mb, mbLinkIndex, 1000 * maxMotorImpulse); mb->getLink(mbLinkIndex).m_userPtr = motor; +#ifndef USE_DISCRETE_DYNAMICS_WORLD m_data->m_dynamicsWorld->addMultiBodyConstraint(motor); +#endif motor->finalizeMultiDof(); } } @@ -3166,11 +3203,15 @@ bool PhysicsServerCommandProcessor::processImportedObjects(const char* fileName, } } UrdfVisualShapeCache* cachedVisualShapesPtr = m_data->m_cachedVUrdfisualShapes[fileName]; + ConvertURDF2Bullet(u2b, creation, rootTrans, m_data->m_dynamicsWorld, useMultiBody, u2b.getPathPrefix(), flags, cachedVisualShapesPtr); + } else { + ConvertURDF2Bullet(u2b, creation, rootTrans, m_data->m_dynamicsWorld, useMultiBody, u2b.getPathPrefix(), flags); + } mb = creation.getBulletMultiBody(); @@ -3788,9 +3829,10 @@ bool PhysicsServerCommandProcessor::processStateLoggingCommand(const struct Shar } } } - +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (clientCmd.m_stateLoggingArguments.m_logType == STATE_LOGGING_GENERIC_ROBOT) { + std::string fileName = clientCmd.m_stateLoggingArguments.m_fileName; int loggerUid = m_data->m_stateLoggersUniqueId++; @@ -3848,6 +3890,7 @@ bool PhysicsServerCommandProcessor::processStateLoggingCommand(const struct Shar serverStatusOut.m_type = CMD_STATE_LOGGING_START_COMPLETED; serverStatusOut.m_stateLoggingResultArgs.m_loggingUniqueId = loggerUid; } +#endif if (clientCmd.m_stateLoggingArguments.m_logType == STATE_LOGGING_VR_CONTROLLERS) { std::string fileName = clientCmd.m_stateLoggingArguments.m_fileName; @@ -4733,7 +4776,11 @@ bool PhysicsServerCommandProcessor::processCreateCollisionShapeCommand(const str bool hasStatus = true; serverStatusOut.m_type = CMD_CREATE_COLLISION_SHAPE_FAILED; +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btWorldImporter* worldImporter = new btWorldImporter(m_data->m_dynamicsWorld); +#else btMultiBodyWorldImporter* worldImporter = new btMultiBodyWorldImporter(m_data->m_dynamicsWorld); +#endif btCollisionShape* shape = 0; b3AlignedObjectArray urdfCollisionObjects; @@ -9444,6 +9491,7 @@ bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct S b3Printf("Step simulation request"); b3Printf("CMD_STEP_FORWARD_SIMULATION clientCmd = %d\n", clientCmd.m_sequenceNumber); } +#ifndef USE_DISCRETE_DYNAMICS_WORLD ///todo(erwincoumans) move this damping inside Bullet for (int i = 0; i < m_data->m_dynamicsWorld->getNumMultibodies(); i++) { @@ -9458,7 +9506,7 @@ bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct S } } } - + #endif btScalar deltaTimeScaled = m_data->m_physicsDeltaTime * simTimeScalingFactor; int numSteps = 0; @@ -9483,8 +9531,9 @@ bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct S serverCmd.m_forwardDynamicsAnalyticsArgs.m_numSteps = numSteps; btAlignedObjectArray islandAnalyticsData; - +#ifndef USE_DISCRETE_DYNAMICS_WORLD m_data->m_dynamicsWorld->getAnalyticsData(islandAnalyticsData); +#endif serverCmd.m_forwardDynamicsAnalyticsArgs.m_numIslands = islandAnalyticsData.size(); int numIslands = btMin(islandAnalyticsData.size(), MAX_ISLANDS_ANALYTICS); @@ -9555,7 +9604,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc if (bodyUniqueId >= 0) { InternalBodyData* body = m_data->m_bodyHandles.getHandle(bodyUniqueId); - +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (body && body->m_multiBody) { btMultiBody* mb = body->m_multiBody; @@ -9741,6 +9790,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc } } } + if (clientCmd.m_updateFlags & CHANGE_DYNAMICS_INFO_SET_JOINT_LIMITS) { @@ -9875,6 +9925,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc } } else +#endif { btRigidBody* rb = 0; if (body && body->m_rigidBody) @@ -10401,7 +10452,11 @@ bool PhysicsServerCommandProcessor::processSendPhysicsParametersCommand(const st btConstraintSolver* oldSolver = m_data->m_dynamicsWorld->getConstraintSolver(); +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btSequentialImpulseConstraintSolver* newSolver = 0; +#else btMultiBodyConstraintSolver* newSolver = 0; +#endif switch (clientCmd.m_physSimParamArgs.m_constraintSolverType) { @@ -10414,7 +10469,12 @@ bool PhysicsServerCommandProcessor::processSendPhysicsParametersCommand(const st case eConstraintSolverLCP_PGS: { btSolveProjectedGaussSeidel* mlcp = new btSolveProjectedGaussSeidel(); +#ifdef USE_DISCRETE_DYNAMICS_WORLD + newSolver = new btMLCPSolver(mlcp); +#else newSolver = new btMultiBodyMLCPConstraintSolver(mlcp); +#endif + b3Printf("PyBullet: Constraint Solver: MLCP + PGS\n"); break; } @@ -10438,7 +10498,11 @@ bool PhysicsServerCommandProcessor::processSendPhysicsParametersCommand(const st { delete oldSolver; +#ifdef USE_DISCRETE_DYNAMICS_WORLD + m_data->m_dynamicsWorld->setConstraintSolver(newSolver); +#else m_data->m_dynamicsWorld->setMultiBodyConstraintSolver(newSolver); +#endif m_data->m_solver = newSolver; printf("switched solver\n"); } @@ -10840,7 +10904,11 @@ bool PhysicsServerCommandProcessor::processCreateRigidBodyCommand(const struct S shapeType = clientCmd.m_createBoxShapeArguments.m_collisionShapeType; } +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btWorldImporter* worldImporter = new btWorldImporter(m_data->m_dynamicsWorld); +#else btMultiBodyWorldImporter* worldImporter = new btMultiBodyWorldImporter(m_data->m_dynamicsWorld); +#endif m_data->m_worldImporters.push_back(worldImporter); btCollisionShape* shape = 0; @@ -11585,6 +11653,7 @@ bool PhysicsServerCommandProcessor::processRemoveBodyCommand(const struct Shared InternalBodyHandle* bodyHandle = m_data->m_bodyHandles.getHandle(bodyUniqueId); if (bodyHandle) { +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (bodyHandle->m_multiBody) { serverCmd.m_removeObjectArgs.m_bodyUniqueIds[serverCmd.m_removeObjectArgs.m_numBodies++] = bodyUniqueId; @@ -11654,6 +11723,7 @@ bool PhysicsServerCommandProcessor::processRemoveBodyCommand(const struct Shared bodyHandle->m_multiBody = 0; serverCmd.m_type = CMD_REMOVE_BODY_COMPLETED; } +#endif if (bodyHandle->m_rigidBody) { if (m_data->m_pluginManager.getRenderInterface()) @@ -11731,7 +11801,11 @@ bool PhysicsServerCommandProcessor::processRemoveBodyCommand(const struct Shared for (int i = 0; i < m_data->m_worldImporters.size(); i++) { +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btWorldImporter* importer = m_data->m_worldImporters[i]; +#else btMultiBodyWorldImporter* importer = m_data->m_worldImporters[i]; +#endif for (int c = 0; c < importer->getNumCollisionShapes(); c++) { if (importer->getCollisionShapeByIndex(c) == handle->m_collisionShape) @@ -11747,7 +11821,11 @@ bool PhysicsServerCommandProcessor::processRemoveBodyCommand(const struct Shared } if (foundIndex >= 0) { +#ifdef USE_DISCRETE_DYNAMICS_WORLD + btWorldImporter* importer = m_data->m_worldImporters[foundIndex]; +#else btMultiBodyWorldImporter* importer = m_data->m_worldImporters[foundIndex]; +#endif m_data->m_worldImporters.removeAtIndex(foundIndex); importer->deleteAllData(); delete importer; @@ -11901,6 +11979,7 @@ bool PhysicsServerCommandProcessor::processCreateUserConstraintCommand(const str { btScalar defaultMaxForce = 500.0; InternalBodyData* parentBody = m_data->m_bodyHandles.getHandle(clientCmd.m_userConstraintArguments.m_parentBodyIndex); +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (parentBody && parentBody->m_multiBody) { if ((clientCmd.m_userConstraintArguments.m_parentJointIndex >= -1) && clientCmd.m_userConstraintArguments.m_parentJointIndex < parentBody->m_multiBody->getNumLinks()) @@ -12054,6 +12133,7 @@ bool PhysicsServerCommandProcessor::processCreateUserConstraintCommand(const str } } else +#endif { InternalBodyData* childBody = clientCmd.m_userConstraintArguments.m_childBodyIndex >= 0 ? m_data->m_bodyHandles.getHandle(clientCmd.m_userConstraintArguments.m_childBodyIndex) : 0; @@ -12301,12 +12381,14 @@ bool PhysicsServerCommandProcessor::processCreateUserConstraintCommand(const str InteralUserConstraintData* userConstraintPtr = m_data->m_userConstraints.find(userConstraintUidRemove); if (userConstraintPtr) { +#ifndef USE_DISCRETE_DYNAMICS_WORLD if (userConstraintPtr->m_mbConstraint) { m_data->m_dynamicsWorld->removeMultiBodyConstraint(userConstraintPtr->m_mbConstraint); delete userConstraintPtr->m_mbConstraint; m_data->m_userConstraints.remove(userConstraintUidRemove); } +#endif//USE_DISCRETE_DYNAMICS_WORLD if (userConstraintPtr->m_rbConstraint) { m_data->m_dynamicsWorld->removeConstraint(userConstraintPtr->m_rbConstraint); @@ -13654,12 +13736,14 @@ bool PhysicsServerCommandProcessor::processRemoveStateCommand(const struct Share bool PhysicsServerCommandProcessor::processRestoreStateCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes) { + BT_PROFILE("CMD_RESTORE_STATE"); bool hasStatus = true; SharedMemoryStatus& serverCmd = serverStatusOut; serverCmd.m_type = CMD_RESTORE_STATE_FAILED; - +#ifndef USE_DISCRETE_DYNAMICS_WORLD btMultiBodyWorldImporter* importer = new btMultiBodyWorldImporter(m_data->m_dynamicsWorld); + importer->setImporterFlags(eRESTORE_EXISTING_OBJECTS); bool ok = false; @@ -13727,7 +13811,7 @@ bool PhysicsServerCommandProcessor::processRestoreStateCommand(const struct Shar { serverCmd.m_type = CMD_RESTORE_STATE_COMPLETED; } - +#endif return hasStatus; } @@ -13739,6 +13823,7 @@ bool PhysicsServerCommandProcessor::processLoadBulletCommand(const struct Shared SharedMemoryStatus& serverCmd = serverStatusOut; serverCmd.m_type = CMD_BULLET_LOADING_FAILED; +#ifndef USE_DISCRETE_DYNAMICS_WORLD //btBulletWorldImporter* importer = new btBulletWorldImporter(m_data->m_dynamicsWorld); btMultiBodyWorldImporter* importer = new btMultiBodyWorldImporter(m_data->m_dynamicsWorld); @@ -13817,6 +13902,7 @@ bool PhysicsServerCommandProcessor::processLoadBulletCommand(const struct Shared m_data->m_guiHelper->autogenerateGraphicsObjects(m_data->m_dynamicsWorld); } } +#endif return hasStatus; } diff --git a/examples/pybullet/gym/pybullet_utils/readwriteurdf.py b/examples/pybullet/gym/pybullet_utils/readwriteurdf.py new file mode 100644 index 000000000..18218e67f --- /dev/null +++ b/examples/pybullet/gym/pybullet_utils/readwriteurdf.py @@ -0,0 +1,25 @@ +from pybullet_utils import bullet_client as bc +from pybullet_utils import urdfEditor as ed +import pybullet +import pybullet_data +import time +import argparse + +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument('--urdf_in', help='urdf file name input', default='test.urdf') +parser.add_argument('--urdf_out', help='urdf file name output', default='out.urdf') +parser.add_argument('--urdf_flags', help='urdf flags', type=int, default=0) + + + +args = parser.parse_args() + + +p0 = bc.BulletClient(connection_mode=pybullet.DIRECT) + +p0.setAdditionalSearchPath(pybullet_data.getDataPath()) +body_id = p0.loadURDF(args.urdf_in, flags = args.urdf_flags) +ed0 = ed.UrdfEditor() +ed0.initializeFromBulletBody(body_id, p0._client) +ed0.saveUrdf(args.urdf_out) + diff --git a/examples/pybullet/gym/pybullet_utils/urdfEditor.py b/examples/pybullet/gym/pybullet_utils/urdfEditor.py index c10e39bff..a9d9c5fa1 100644 --- a/examples/pybullet/gym/pybullet_utils/urdfEditor.py +++ b/examples/pybullet/gym/pybullet_utils/urdfEditor.py @@ -152,6 +152,7 @@ class UrdfEditor(object): baseLinkIndex = -1 self.convertLinkFromMultiBody(bodyUid, baseLinkIndex, baseLink, physicsClientId) baseLink.link_name = p.getBodyInfo(bodyUid, physicsClientId=physicsClientId)[0].decode("utf-8") + self.robotName = p.getBodyInfo(bodyUid, physicsClientId=physicsClientId)[1].decode("utf-8") self.linkNameToIndex[baseLink.link_name] = len(self.urdfLinks) self.urdfLinks.append(baseLink) diff --git a/src/BulletDynamics/MLCPSolvers/btMLCPSolver.cpp b/src/BulletDynamics/MLCPSolvers/btMLCPSolver.cpp index 5d864f275..ed4e0b686 100644 --- a/src/BulletDynamics/MLCPSolvers/btMLCPSolver.cpp +++ b/src/BulletDynamics/MLCPSolvers/btMLCPSolver.cpp @@ -532,7 +532,7 @@ void btMLCPSolver::createMLCP(const btContactSolverInfo& infoGlobal) J_transpose = J.transpose(); btMatrixXu& tmp = m_scratchTmp; - + //Minv.printMatrix("Minv="); { { BT_PROFILE("J*Minv"); @@ -543,7 +543,7 @@ void btMLCPSolver::createMLCP(const btContactSolverInfo& infoGlobal) m_A = tmp * J_transpose; } } - + //J.printMatrix("J"); if (1) { // add cfm to the diagonal of m_A diff --git a/src/BulletSoftBody/btDeformableMousePickingForce.h b/src/BulletSoftBody/btDeformableMousePickingForce.h index d218d9621..697408355 100644 --- a/src/BulletSoftBody/btDeformableMousePickingForce.h +++ b/src/BulletSoftBody/btDeformableMousePickingForce.h @@ -29,7 +29,7 @@ class btDeformableMousePickingForce : public btDeformableLagrangianForce public: typedef btAlignedObjectArray TVStack; - btDeformableMousePickingForce(btScalar k, btScalar d, const btSoftBody::Face& face, btVector3 mouse_pos, btScalar maxForce = 0.3) : m_elasticStiffness(k), m_dampingStiffness(d), m_face(face), m_mouse_pos(mouse_pos), m_maxForce(maxForce) + btDeformableMousePickingForce(btScalar k, btScalar d, const btSoftBody::Face& face, const btVector3& mouse_pos, btScalar maxForce = 0.3) : m_elasticStiffness(k), m_dampingStiffness(d), m_face(face), m_mouse_pos(mouse_pos), m_maxForce(maxForce) { } From 6f7d458679942f04e56897bcbe2c8f798764b14d Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Tue, 16 Mar 2021 18:17:23 -0700 Subject: [PATCH 23/36] add missing line in previous commit --- examples/SharedMemory/PhysicsServerCommandProcessor.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 684fd555b..0cb66f157 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -2730,10 +2730,13 @@ void PhysicsServerCommandProcessor::createEmptyDynamicsWorld(int flags) #endif } + + if ((0 == m_data->m_dynamicsWorld) && (0 == (flags & RESET_USE_DISCRETE_DYNAMICS_WORLD))) { #ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD + m_data->m_solver = new btMultiBodyConstraintSolver; m_data->m_dynamicsWorld = new btSoftMultiBodyDynamicsWorld(m_data->m_dispatcher, m_data->m_broadphase, m_data->m_solver, m_data->m_collisionConfiguration); #else #ifdef USE_DISCRETE_DYNAMICS_WORLD From 6e2577ca598cbde68ccf3b066dd13253aeeea91e Mon Sep 17 00:00:00 2001 From: Chris Djali Date: Sun, 21 Mar 2021 15:07:01 +0000 Subject: [PATCH 24/36] Use drawTriangle instead of drawing three lines --- src/BulletCollision/CollisionDispatch/btCollisionWorld.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/BulletCollision/CollisionDispatch/btCollisionWorld.cpp b/src/BulletCollision/CollisionDispatch/btCollisionWorld.cpp index 71184f36a..7cc6b8ab0 100644 --- a/src/BulletCollision/CollisionDispatch/btCollisionWorld.cpp +++ b/src/BulletCollision/CollisionDispatch/btCollisionWorld.cpp @@ -1294,9 +1294,7 @@ public: btVector3 normalColor(1, 1, 0); m_debugDrawer->drawLine(center, center + normal, normalColor); } - m_debugDrawer->drawLine(wv0, wv1, m_color); - m_debugDrawer->drawLine(wv1, wv2, m_color); - m_debugDrawer->drawLine(wv2, wv0, m_color); + m_debugDrawer->drawTriangle(wv0, wv1, wv2, m_color, 1.0); } }; From 546212b15892e6ae83e47e5bb1db9dfce7a15f3a Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Mon, 22 Mar 2021 16:53:17 -0700 Subject: [PATCH 25/36] add mug obj --- .../gym/pybullet_data/objects/mug.obj | 1352 +++++++++++++++++ .../gym/pybullet_data/objects/mug.urdf | 28 + .../gym/pybullet_data/objects/mug_col.obj | 1310 ++++++++++++++++ 3 files changed, 2690 insertions(+) create mode 100644 examples/pybullet/gym/pybullet_data/objects/mug.obj create mode 100644 examples/pybullet/gym/pybullet_data/objects/mug.urdf create mode 100644 examples/pybullet/gym/pybullet_data/objects/mug_col.obj diff --git a/examples/pybullet/gym/pybullet_data/objects/mug.obj b/examples/pybullet/gym/pybullet_data/objects/mug.obj new file mode 100644 index 000000000..4fb8d9186 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/objects/mug.obj @@ -0,0 +1,1352 @@ +# Blender v2.79 (sub 0) OBJ File: 'mug.blend' +# www.blender.org +o mug_Cylinder.002 +v 0.000000 0.000000 0.000000 +v 0.000000 0.032649 0.008628 +v 0.000000 -0.000000 0.008628 +v 0.008450 0.031536 0.008628 +v 0.016324 0.028275 0.008628 +v 0.023086 0.023086 0.008628 +v 0.028275 0.016324 0.008628 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360//373 361//405 408//408 +f 363//362 377//361 408//408 +f 359//390 362//389 408//408 +f 379//403 378//388 408//408 +f 362//389 364//396 408//408 +f 378//388 380//387 408//408 +f 380//387 382//406 408//408 From c810edf483281cd73fdc296c58512f03184a10ec Mon Sep 17 00:00:00 2001 From: Kieren Date: Thu, 25 Mar 2021 21:00:27 +1000 Subject: [PATCH 26/36] Updating a misleading comment --- src/LinearMath/btIDebugDraw.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/LinearMath/btIDebugDraw.h b/src/LinearMath/btIDebugDraw.h index 82ec19a69..df4db2ff5 100644 --- a/src/LinearMath/btIDebugDraw.h +++ b/src/LinearMath/btIDebugDraw.h @@ -4,8 +4,8 @@ Copyright (c) 2003-2009 Erwin Coumans http://bulletphysics.org This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software. -Permission is granted to anyone to use this software for any purpose, -including commercial applications, and to alter it and redistribute it freely, +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions: 1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. @@ -21,7 +21,7 @@ subject to the following restrictions: ///The btIDebugDraw interface class allows hooking up a debug renderer to visually debug simulations. ///Typical use case: create a debug drawer object, and assign it to a btCollisionWorld or btDynamicsWorld using setDebugDrawer and call debugDrawWorld. -///A class that implements the btIDebugDraw interface has to implement the drawLine method at a minimum. +///A class that implements the btIDebugDraw interface will need to provide non-empty implementations of the the drawLine and getDebugMode methods at a minimum. ///For color arguments the X,Y,Z components refer to Red, Green and Blue each in the range [0..1] class btIDebugDraw { From 0196edd4d2e0cde2de5c8138d5f69df2cea9fce4 Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Fri, 26 Mar 2021 09:11:59 -0700 Subject: [PATCH 27/36] update single aabb, during resetJointState/resetBasePositionAndOrientation, to allow raycast/collision queries. --- examples/SharedMemory/PhysicsServerCommandProcessor.cpp | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 0cb66f157..9cb5c3455 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -10785,6 +10785,12 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe scratch_m.resize(nLinks + 1); mb->updateCollisionObjectWorldTransforms(scratch_q, scratch_m); + + m_data->m_dynamicsWorld->updateSingleAabb(mb->getBaseCollider()); + for (int i=0;igetNumLinks();i++) + { + m_data->m_dynamicsWorld->updateSingleAabb(mb->getLinkCollider(i)); + } } if (body && body->m_rigidBody) @@ -10809,6 +10815,7 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe body->m_rigidBody->getWorldTransform().setRotation(baseOrn); body->m_rigidBody->setAngularVelocity(baseAngVel); } + m_data->m_dynamicsWorld->updateSingleAabb(body->m_rigidBody); } #ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD if (body && body->m_softBody) @@ -10835,6 +10842,7 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe } body->m_softBody->transformTo(tr); } + m_data->m_dynamicsWorld->updateSingleAabb(body->m_softBody); } #endif syncPhysicsToGraphics2(); From d1c4c41b9a35ac7f09e39f2453a3e5700995ee27 Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Fri, 26 Mar 2021 10:31:13 -0700 Subject: [PATCH 28/36] add performCollisionDetection (stepSimulation also calls this, but does more, including solving constraints and integration) --- examples/SharedMemory/PhysicsClientC_API.cpp | 13 +++++++ examples/SharedMemory/PhysicsClientC_API.h | 2 + .../PhysicsClientSharedMemory.cpp | 10 +++++ examples/SharedMemory/PhysicsDirect.cpp | 4 ++ .../PhysicsServerCommandProcessor.cpp | 23 +++++++++++ .../PhysicsServerCommandProcessor.h | 1 + examples/SharedMemory/SharedMemoryPublic.h | 4 +- examples/pybullet/pybullet.c | 39 +++++++++++++++++++ 8 files changed, 95 insertions(+), 1 deletion(-) diff --git a/examples/SharedMemory/PhysicsClientC_API.cpp b/examples/SharedMemory/PhysicsClientC_API.cpp index 829eeca56..99b907a70 100644 --- a/examples/SharedMemory/PhysicsClientC_API.cpp +++ b/examples/SharedMemory/PhysicsClientC_API.cpp @@ -977,6 +977,19 @@ B3_SHARED_API b3SharedMemoryCommandHandle b3InitStepSimulationCommand2(b3SharedM return (b3SharedMemoryCommandHandle)command; } + +B3_SHARED_API b3SharedMemoryCommandHandle b3InitPerformCollisionDetectionCommand(b3PhysicsClientHandle physClient) +{ + PhysicsClient* cl = (PhysicsClient*)physClient; + b3Assert(cl); + b3Assert(cl->canSubmitCommand()); + struct SharedMemoryCommand* command = cl->getAvailableSharedMemoryCommand(); + b3Assert(command); + command->m_type = CMD_PERFORM_COLLISION_DETECTION; + command->m_updateFlags = 0; + return (b3SharedMemoryCommandHandle)command; +} + B3_SHARED_API b3SharedMemoryCommandHandle b3InitResetSimulationCommand(b3PhysicsClientHandle physClient) { PhysicsClient* cl = (PhysicsClient*)physClient; diff --git a/examples/SharedMemory/PhysicsClientC_API.h b/examples/SharedMemory/PhysicsClientC_API.h index 318263b2d..4727d6f86 100644 --- a/examples/SharedMemory/PhysicsClientC_API.h +++ b/examples/SharedMemory/PhysicsClientC_API.h @@ -382,6 +382,8 @@ extern "C" B3_SHARED_API b3SharedMemoryCommandHandle b3InitStepSimulationCommand(b3PhysicsClientHandle physClient); B3_SHARED_API b3SharedMemoryCommandHandle b3InitStepSimulationCommand2(b3SharedMemoryCommandHandle commandHandle); + B3_SHARED_API b3SharedMemoryCommandHandle b3InitPerformCollisionDetectionCommand(b3PhysicsClientHandle physClient); + B3_SHARED_API int b3GetStatusForwardDynamicsAnalyticsData(b3SharedMemoryStatusHandle statusHandle, struct b3ForwardDynamicsAnalyticsArgs* analyticsData); diff --git a/examples/SharedMemory/PhysicsClientSharedMemory.cpp b/examples/SharedMemory/PhysicsClientSharedMemory.cpp index bf1096e05..456a1c4be 100644 --- a/examples/SharedMemory/PhysicsClientSharedMemory.cpp +++ b/examples/SharedMemory/PhysicsClientSharedMemory.cpp @@ -702,6 +702,16 @@ const SharedMemoryStatus* PhysicsClientSharedMemory::processServerStatus() } break; } + case CMD_PERFORM_COLLISION_DETECTION_COMPLETED: + { + B3_PROFILE("CMD_PERFORM_COLLISION_DETECTION_COMPLETED"); + + if (m_data->m_verboseOutput) + { + b3Printf("Server completed performing collision detection"); + } + break; + } case CMD_URDF_LOADING_FAILED: { B3_PROFILE("CMD_URDF_LOADING_FAILED"); diff --git a/examples/SharedMemory/PhysicsDirect.cpp b/examples/SharedMemory/PhysicsDirect.cpp index ab6ae5ba2..69490ed77 100644 --- a/examples/SharedMemory/PhysicsDirect.cpp +++ b/examples/SharedMemory/PhysicsDirect.cpp @@ -1166,6 +1166,10 @@ void PhysicsDirect::postProcessStatus(const struct SharedMemoryStatus& serverCmd { break; } + case CMD_PERFORM_COLLISION_DETECTION_COMPLETED: + { + break; + } case CMD_REQUEST_PHYSICS_SIMULATION_PARAMETERS_COMPLETED: { break; diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 9cb5c3455..14f1e2b4b 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -9483,6 +9483,24 @@ bool PhysicsServerCommandProcessor::processRequestCollisionInfoCommand(const str return hasStatus; } +bool PhysicsServerCommandProcessor::performCollisionDetectionCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes) +{ + bool hasStatus = true; + + BT_PROFILE("CMD_PERFORM_COLLISION_DETECTION"); + + if (m_data->m_verboseOutput) + { + b3Printf("Perform Collision Detection command"); + b3Printf("CMD_PERFORM_COLLISION_DETECTION clientCmd = %d\n", clientCmd.m_sequenceNumber); + } + + m_data->m_dynamicsWorld->performDiscreteCollisionDetection(); + SharedMemoryStatus& serverCmd = serverStatusOut; + serverCmd.m_type = CMD_PERFORM_COLLISION_DETECTION_COMPLETED; + return true; +} + bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes) { bool hasStatus = true; @@ -14131,6 +14149,11 @@ bool PhysicsServerCommandProcessor::processCommand(const struct SharedMemoryComm hasStatus = processForwardDynamicsCommand(clientCmd, serverStatusOut, bufferServerToClient, bufferSizeInBytes); break; } + case CMD_PERFORM_COLLISION_DETECTION: + { + hasStatus = performCollisionDetectionCommand(clientCmd, serverStatusOut, bufferServerToClient, bufferSizeInBytes); + break; + } case CMD_REQUEST_INTERNAL_DATA: { diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.h b/examples/SharedMemory/PhysicsServerCommandProcessor.h index cbc4cbab4..d456675df 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.h +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.h @@ -55,6 +55,7 @@ protected: bool processProfileTimingCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); bool processRequestCollisionInfoCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); bool processForwardDynamicsCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); + bool performCollisionDetectionCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); bool processRequestInternalDataCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); bool processChangeDynamicsInfoCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); bool processSetAdditionalSearchPathCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes); diff --git a/examples/SharedMemory/SharedMemoryPublic.h b/examples/SharedMemory/SharedMemoryPublic.h index 775f9f708..9ea8c1694 100644 --- a/examples/SharedMemory/SharedMemoryPublic.h +++ b/examples/SharedMemory/SharedMemoryPublic.h @@ -115,7 +115,8 @@ enum EnumSharedMemoryClientCommand CMD_REMOVE_USER_DATA, CMD_COLLISION_FILTER, CMD_REQUEST_MESH_DATA, - + + CMD_PERFORM_COLLISION_DETECTION, //don't go beyond this command! CMD_MAX_CLIENT_COMMANDS, }; @@ -239,6 +240,7 @@ enum EnumSharedMemoryServerStatus CMD_REQUEST_MESH_DATA_COMPLETED, CMD_REQUEST_MESH_DATA_FAILED, + CMD_PERFORM_COLLISION_DETECTION_COMPLETED, //don't go beyond 'CMD_MAX_SERVER_COMMANDS! CMD_MAX_SERVER_COMMANDS }; diff --git a/examples/pybullet/pybullet.c b/examples/pybullet/pybullet.c index 56b5f7511..7359fdadc 100644 --- a/examples/pybullet/pybullet.c +++ b/examples/pybullet/pybullet.c @@ -372,6 +372,41 @@ static PyObject* pybullet_stepSimulation(PyObject* self, PyObject* args, PyObjec return Py_None; } + +// perform collision detection: update aabbs, compute overlapping pairs and contact points +static PyObject* pybullet_performCollisionDetection(PyObject* self, PyObject* args, PyObject* keywds) +{ + int physicsClientId = 0; + static char* kwlist[] = {"physicsClientId", NULL}; + b3PhysicsClientHandle sm = 0; + + if (!PyArg_ParseTupleAndKeywords(args, keywds, "|i", kwlist, &physicsClientId)) + { + return NULL; + } + sm = getPhysicsClient(physicsClientId); + if (sm == 0) + { + PyErr_SetString(SpamError, "Not connected to physics server."); + return NULL; + } + + { + b3SharedMemoryStatusHandle statusHandle; + int statusType; + + if (b3CanSubmitCommand(sm)) + { + statusHandle = b3SubmitClientCommandAndWaitStatus( + sm, b3InitPerformCollisionDetectionCommand(sm)); + } + } + + Py_INCREF(Py_None); + return Py_None; +} + + static PyObject* pybullet_connectPhysicsServer(PyObject* self, PyObject* args, PyObject* keywds) { int freeIndex = -1; @@ -12244,6 +12279,10 @@ static PyMethodDef SpamMethods[] = { "stepSimulation(physicsClientId=0)\n" "Step the simulation using forward dynamics."}, + {"performCollisionDetection", (PyCFunction)pybullet_performCollisionDetection, METH_VARARGS | METH_KEYWORDS, + "performCollisionDetection(physicsClientId=0)\n" + "Update AABBs, compute overlapping pairs and contact points. stepSimulation also includes this already."}, + {"setGravity", (PyCFunction)pybullet_setGravity, METH_VARARGS | METH_KEYWORDS, "setGravity(gravX, gravY, gravZ, physicsClientId=0)\n" "Set the gravity acceleration (x,y,z)."}, From 4d34ba731094a3e08fc88f8ff898d901788a0058 Mon Sep 17 00:00:00 2001 From: erwin coumans Date: Mon, 5 Apr 2021 11:35:19 -0700 Subject: [PATCH 29/36] add updated Laikago envs_v2, with PMTG and MPC examples. python -m pybullet_envs.minitaur.envs_v2.locomotion_gym_env_test python -m pybullet_envs.minitaur.envs_v2.examples.laikago_mpc_wrapper_example python -m pybullet_envs.minitaur.envs_v2.examples.laikago_static_gait_example python -m pybullet_envs.minitaur.agents.baseline_controller.locomotion_controller_example PMTG example, not well tuned: python -m pybullet_envs.minitaur.envs_v2.examples.laikago_pmtg_example --- .../configs/laikago_gym_config.gin | 80 + .../pybullet_data/configs/laikago_gym_env.gin | 116 ++ .../configs/laikago_mpc_example_flat.gin | 149 ++ .../configs/laikago_mpc_stepstone.gin | 142 ++ .../configs/laikago_reactive.gin | 133 ++ .../configs/laikago_walk_static_gait.gin | 65 + .../configs/minitaur_gym_config.gin | 48 + .../configs/minitaur_gym_env.gin | 63 + .../configs_v2/base/laikago_reactive.gin | 80 + .../configs_v2/base/laikago_with_imu.gin | 66 + .../configs_v2/base/mini_cheetah_with_imu.gin | 9 + .../configs_v2/robots/laikago.gin | 58 + .../configs_v2/robots/mini_cheetah.gin | 98 ++ .../configs_v2/robots/minitaur.gin | 31 + .../configs_v2/scenes/simple_scene.gin | 4 + .../configs_v2/scenes/stair_scene.gin | 3 + .../configs_v2/tasks/fwd_task.gin | 4 + .../configs_v2/tasks/fwd_task_laikago.gin | 8 + .../tasks/fwd_task_no_termination.gin | 11 + .../fwd_task_no_termination_simplified.gin | 12 + .../configs_v2/wrappers/pmtg_wrapper.gin | 18 + .../configs_v2/wrappers/pmtg_wrapper_dict.gin | 6 + .../wrappers/pmtg_wrapper_laikago.gin | 10 + .../wrappers/pmtg_wrapper_simplified_env.gin | 18 + .../gym/pybullet_data/testdata/__init__.py | 0 .../test_imu_state_estimator/__init__.py | 0 .../accelerometer.npy | Bin 0 -> 4928 bytes .../estimated_velocities.npy | Bin 0 -> 4928 bytes .../feet_contact_forces.npy | Bin 0 -> 19328 bytes .../test_imu_state_estimator/gyroscope.npy | Bin 0 -> 6528 bytes .../test_imu_state_estimator/jacobians.npy | Bin 0 -> 201440 bytes .../motor_velocities.npy | Bin 0 -> 19328 bytes .../test_imu_state_estimator/timestamp.npy | Bin 0 -> 1728 bytes .../pybullet/gym/pybullet_data/urdf/mug.obj | 1352 +++++++++++++++ .../pybullet/gym/pybullet_data/urdf/mug.urdf | 28 + .../gym/pybullet_data/urdf/mug_col.obj | 1310 +++++++++++++++ .../gym/pybullet_envs/minitaur/__init__.py | 1 - .../pybullet_envs/minitaur/agents/__init__.py | 1 - .../agents/baseline_controller/__init__.py | 0 .../com_height_estimator.py | 76 + .../com_velocity_estimator.py | 83 + .../dummy_gait_generator.py | 87 + .../baseline_controller/foot_stepper.py | 199 +++ .../baseline_controller/gait_generator.py | 32 + .../imu_based_com_velocity_estimator.py | 216 +++ .../baseline_controller/leg_controller.py | 29 + .../locomotion_controller.py | 96 ++ .../locomotion_controller_example.py | 190 +++ ...tion_controller_in_scenario_set_example.py | 180 ++ .../locomotion_controller_setup.py | 175 ++ .../minitaur_raibert_controller.py | 448 +++++ .../minitaur_raibert_controller_utils.py | 82 + .../model_predictive_control.py | 149 ++ .../multi_state_estimator.py | 48 + .../openloop_gait_generator.py | 194 +++ .../raibert_swing_leg_controller.py | 242 +++ .../baseline_controller/state_estimator.py | 21 + .../static_gait_controller.py | 63 + .../time_based_moving_window_filter.py | 45 + .../torque_stance_leg_controller.py | 229 +++ .../trajectory_generator/controller_simple.py | 87 + .../agents/trajectory_generator/tg_inplace.py | 57 + .../agents/trajectory_generator/tg_simple.py | 400 +++++ .../minitaur/envs_v2/__init__.py | 0 .../minitaur/envs_v2/base_client.py | 63 + .../envs_v2/curriculum_reset_helpers.py | 62 + .../minitaur/envs_v2/env_loader.py | 75 + .../minitaur/envs_v2/env_wrappers/__init__.py | 0 .../action_denormalize_wrapper.py | 41 + .../env_wrappers/alternating_legs_openloop.py | 125 ++ .../env_wrappers/curriculum_wrapper_env.py | 50 + .../depth_uv_to_footplacement_wrapper.py | 357 ++++ .../fixed_steptime_wrapper_env.py | 92 + .../env_wrappers/ik_based_wrapper_env.py | 109 ++ .../env_wrappers/imitation_wrapper_env.py | 101 ++ .../env_wrappers/mpc_locomotion_wrapper.py | 794 +++++++++ ...observation_dictionary_to_array_wrapper.py | 65 + .../env_wrappers/pmtg_inplace_wrapper_env.py | 176 ++ .../envs_v2/env_wrappers/pmtg_wrapper_env.py | 290 ++++ .../envs_v2/env_wrappers/simple_openloop.py | 127 ++ .../state_machine_based_wrapper_env.py | 321 ++++ .../step_based_curriculum_wrapper_env.py | 231 +++ .../trajectory_generator_wrapper_env.py | 81 + .../envs_v2/env_wrappers/walking_wrapper.py | 134 ++ .../minitaur/envs_v2/evaluation/__init__.py | 0 .../minitaur/envs_v2/evaluation/metric.py | 125 ++ .../envs_v2/evaluation/metric_logger.py | 155 ++ .../envs_v2/evaluation/metric_utils.py | 29 + .../examples/laikago_mpc_wrapper_example.py | 96 ++ .../envs_v2/examples/laikago_pmtg_example.py | 84 + .../examples/laikago_static_gait_example.py | 52 + .../minitaur/envs_v2/locomotion_gym_config.py | 58 + .../envs_v2/locomotion_gym_config_test.py | 75 + .../minitaur/envs_v2/locomotion_gym_env.py | 731 ++++++++ .../envs_v2/locomotion_gym_env_test.py | 241 +++ .../envs_v2/multiagent_mobility_gym_env.py | 485 ++++++ .../minitaur/envs_v2/scenes/scene_base.py | 256 +++ .../minitaur/envs_v2/scenes/simple_scene.py | 35 + .../minitaur/envs_v2/scenes/world_asset.proto | 86 + .../envs_v2/scenes/world_asset_pb2.py | 555 +++++++ .../envs_v2/sensors/accelerometer_sensor.py | 86 + .../minitaur/envs_v2/sensors/camera_sensor.py | 184 ++ .../minitaur/envs_v2/sensors/imu_sensor.py | 133 ++ .../envs_v2/sensors/last_action_sensor.py | 60 + .../envs_v2/sensors/motor_angle_sensor.py | 122 ++ .../minitaur/envs_v2/sensors/sensor.py | 451 +++++ .../minitaur/envs_v2/sensors/space_utils.py | 137 ++ .../envs_v2/sensors/toe_position_sensor.py | 100 ++ .../minitaur/envs_v2/tasks/__init__.py | 0 .../envs_v2/tasks/simple_locomotion_task.py | 125 ++ .../minitaur/envs_v2/tasks/task_interface.py | 37 + .../minitaur/envs_v2/tasks/task_utils.py | 108 ++ .../envs_v2/tasks/terminal_conditions.py | 301 ++++ .../minitaur/envs_v2/utilities/env_utils.py | 175 ++ .../envs_v2/utilities/env_utils_v2.py | 22 + .../envs_v2/utilities/laikago_pose_utils.py | 50 + .../utilities/mini_cheetah_pose_utils.py | 35 + .../envs_v2/utilities/minitaur_pose_utils.py | 187 +++ .../envs_v2/utilities/noise_generators.py | 255 +++ .../envs_v2/utilities/rendering_utils.py | 272 +++ .../envs_v2/utilities/robot_pose_utils.py | 189 +++ .../envs_v2/utilities/termination_reason.py | 40 + .../pybullet_envs/minitaur/robots/__init__.py | 0 .../minitaur/robots/autonomous_object.py | 286 ++++ .../minitaur/robots/crowd_controller.py | 706 ++++++++ .../minitaur/robots/hybrid_motor_model.py | 278 ++++ .../pybullet_envs/minitaur/robots/laikago.py | 319 ++++ .../minitaur/robots/laikago_constants.py | 120 ++ .../minitaur/robots/laikago_interface.proto | 181 ++ .../minitaur/robots/laikago_interface_pb2.py | 1040 ++++++++++++ .../robots/laikago_kinematic_constants.py | 106 ++ .../minitaur/robots/laikago_motor.py | 149 ++ .../minitaur/robots/laikago_v2.py | 34 + .../minitaur/robots/mini_cheetah.py | 133 ++ .../minitaur/robots/mini_cheetah_test.py | 58 + .../pybullet_envs/minitaur/robots/minitaur.py | 1479 +++++++++++++++++ .../minitaur/robots/minitaur_constants.py | 62 + .../minitaur/robots/minitaur_motor.py | 171 ++ .../robots/minitaur_motor_model_v2.py | 145 ++ .../minitaur/robots/minitaur_v2.py | 122 ++ .../minitaur/robots/object_controller.py | 810 +++++++++ .../minitaur/robots/quadruped_base.py | 724 ++++++++ .../minitaur/robots/robot_base.py | 133 ++ .../minitaur/robots/robot_config.py | 105 ++ .../minitaur/robots/robot_urdf_loader.py | 371 +++++ .../minitaur/robots/safety/__init__.py | 0 .../minitaur/robots/safety/data_types.py | 67 + .../robots/safety/motor_action_validator.py | 128 ++ .../robots/safety/motor_state_validator.py | 137 ++ .../safety/python/moving_window_filter.py | 68 + .../minitaur/robots/safety/safety_checker.py | 119 ++ .../minitaur/robots/safety/safety_error.py | 11 + .../minitaur/robots/safety/utilities.py | 163 ++ .../minitaur/robots/time_ordered_buffer.py | 263 +++ .../minitaur/robots/timestamp.proto | 147 ++ .../minitaur/robots/timestamp_pb2.py | 78 + .../minitaur/robots/utilities/__init__.py | 0 .../robots/utilities/action_filter.py | 239 +++ .../minitaur/robots/utilities/kinematics.py | 127 ++ .../robots/utilities/kinematics_utils.py | 208 +++ .../minitaur/robots/utilities/urdf_utils.py | 59 + .../minitaur/robots/vector.proto | 84 + .../minitaur/robots/vector_pb2.py | 640 +++++++ .../minitaur/vision/imagery.proto | 72 + .../minitaur/vision/imagery_client.py | 137 ++ .../minitaur/vision/imagery_utils.py | 43 + .../minitaur/vision/imagery_utils_test.py | 84 + .../minitaur/vision/point_cloud_utils.py | 60 + .../minitaur/vision/sim_camera.proto | 26 + .../minitaur/vision/sim_camera.py | 350 ++++ 170 files changed, 27618 insertions(+), 2 deletions(-) create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_gym_config.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_gym_env.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_mpc_example_flat.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_mpc_stepstone.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_reactive.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/laikago_walk_static_gait.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/minitaur_gym_config.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs/minitaur_gym_env.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_reactive.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_with_imu.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/base/mini_cheetah_with_imu.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/robots/laikago.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/robots/mini_cheetah.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/robots/minitaur.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/scenes/simple_scene.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/scenes/stair_scene.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_laikago.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination_simplified.gin create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper.gin 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100644 examples/pybullet/gym/pybullet_envs/minitaur/vision/point_cloud_utils.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/vision/sim_camera.proto create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/vision/sim_camera.py diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_gym_config.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_gym_config.gin new file mode 100644 index 000000000..3a7095680 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_gym_config.gin @@ -0,0 +1,80 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.sensors.robot_sensors + +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 +NUM_MOTORS = 12 +NOISY_READING = True + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +robot_sensors.IMUSensor.channels = ["R", "P", "dR", "dP"] +robot_sensors.IMUSensor.noisy_reading = %NOISY_READING +robot_sensors.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +robot_sensors.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +robot_sensors.MotorAngleSensor.num_motors = %NUM_MOTORS +robot_sensors.MotorAngleSensor.noisy_reading = %NOISY_READING +robot_sensors.MotorAngleSensor.lower_bound = -6.28318548203 +robot_sensors.MotorAngleSensor.upper_bound = 6.28318548203 + +sensors = [@robot_sensors.IMUSensor(), @robot_sensors.MotorAngleSensor()] + +Act0/locomotion_gym_config.ScalarField.name = "motor_angle_0" +Act0/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act0/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act1/locomotion_gym_config.ScalarField.name = "motor_angle_1" +Act1/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act1/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act2/locomotion_gym_config.ScalarField.name = "motor_angle_2" +Act2/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act2/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act3/locomotion_gym_config.ScalarField.name = "motor_angle_3" +Act3/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act3/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act4/locomotion_gym_config.ScalarField.name = "motor_angle_4" +Act4/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act4/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act5/locomotion_gym_config.ScalarField.name = "motor_angle_5" +Act5/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act5/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act6/locomotion_gym_config.ScalarField.name = "motor_angle_6" +Act6/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act6/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act7/locomotion_gym_config.ScalarField.name = "motor_angle_7" +Act7/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act7/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act8/locomotion_gym_config.ScalarField.name = "motor_angle_8" +Act8/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act8/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act9/locomotion_gym_config.ScalarField.name = "motor_angle_9" +Act9/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act9/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act10/locomotion_gym_config.ScalarField.name = "motor_angle_10" +Act10/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act10/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act11/locomotion_gym_config.ScalarField.name = "motor_angle_11" +Act11/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act11/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND + + +locomotion_gym_config.LocomotionGymConfig.actions = [ + @Act0/locomotion_gym_config.ScalarField(), + @Act1/locomotion_gym_config.ScalarField(), + @Act2/locomotion_gym_config.ScalarField(), + @Act3/locomotion_gym_config.ScalarField(), + @Act4/locomotion_gym_config.ScalarField(), + @Act5/locomotion_gym_config.ScalarField(), + @Act6/locomotion_gym_config.ScalarField(), + @Act7/locomotion_gym_config.ScalarField(), + @Act8/locomotion_gym_config.ScalarField(), + @Act9/locomotion_gym_config.ScalarField(), + @Act10/locomotion_gym_config.ScalarField(), + @Act11/locomotion_gym_config.ScalarField()] diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_gym_env.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_gym_env.gin new file mode 100644 index 000000000..0b3b1ce95 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_gym_env.gin @@ -0,0 +1,116 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.robots.laikago + +URDF_ROOT = "urdf/" +ABDUCTION_P_GAIN = 220.0 +ABDUCTION_D_GAIN = 0.3 +HIP_P_GAIN = 220.0 +HIP_D_GAIN = 2.0 +KNEE_P_GAIN = 220.0 +KNEE_D_GAIN = 2.0 + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.sensors.robot_sensors + +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 +NUM_MOTORS = 12 +NOISY_READING = True + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +robot_sensors.IMUSensor.channels = ["R", "P", "dR", "dP"] +robot_sensors.IMUSensor.noisy_reading = %NOISY_READING +robot_sensors.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +robot_sensors.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +robot_sensors.MotorAngleSensor.num_motors = %NUM_MOTORS +robot_sensors.MotorAngleSensor.noisy_reading = %NOISY_READING +robot_sensors.MotorAngleSensor.lower_bound = -6.28318548203 +robot_sensors.MotorAngleSensor.upper_bound = 6.28318548203 + +sensors = [@robot_sensors.IMUSensor(), @robot_sensors.MotorAngleSensor()] + +Act0/locomotion_gym_config.ScalarField.name = "motor_angle_0" +Act0/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act0/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act1/locomotion_gym_config.ScalarField.name = "motor_angle_1" +Act1/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act1/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act2/locomotion_gym_config.ScalarField.name = "motor_angle_2" +Act2/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act2/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act3/locomotion_gym_config.ScalarField.name = "motor_angle_3" +Act3/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act3/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act4/locomotion_gym_config.ScalarField.name = "motor_angle_4" +Act4/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act4/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act5/locomotion_gym_config.ScalarField.name = "motor_angle_5" +Act5/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act5/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act6/locomotion_gym_config.ScalarField.name = "motor_angle_6" +Act6/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act6/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act7/locomotion_gym_config.ScalarField.name = "motor_angle_7" +Act7/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act7/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act8/locomotion_gym_config.ScalarField.name = "motor_angle_8" +Act8/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act8/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act9/locomotion_gym_config.ScalarField.name = "motor_angle_9" +Act9/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act9/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act10/locomotion_gym_config.ScalarField.name = "motor_angle_10" +Act10/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act10/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act11/locomotion_gym_config.ScalarField.name = "motor_angle_11" +Act11/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act11/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND + + +locomotion_gym_config.LocomotionGymConfig.actions = [ + @Act0/locomotion_gym_config.ScalarField(), + @Act1/locomotion_gym_config.ScalarField(), + @Act2/locomotion_gym_config.ScalarField(), + @Act3/locomotion_gym_config.ScalarField(), + @Act4/locomotion_gym_config.ScalarField(), + @Act5/locomotion_gym_config.ScalarField(), + @Act6/locomotion_gym_config.ScalarField(), + @Act7/locomotion_gym_config.ScalarField(), + @Act8/locomotion_gym_config.ScalarField(), + @Act9/locomotion_gym_config.ScalarField(), + @Act10/locomotion_gym_config.ScalarField(), + @Act11/locomotion_gym_config.ScalarField()] + + + +laikago.Laikago.urdf_root = %URDF_ROOT +laikago.Laikago.time_step = %SIM_TIME_STEP +laikago.Laikago.action_repeat = %NUM_ACTION_REPEAT +laikago.Laikago.self_collision_enabled = False +laikago.Laikago.control_latency = 0.002 +laikago.Laikago.pd_latency = 0.0 +laikago.Laikago.motor_kp = [%ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN] +laikago.Laikago.motor_kd = [%ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN] +laikago.Laikago.sensors = %sensors + +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago.Laikago +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_example_flat.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_example_flat.gin new file mode 100644 index 000000000..df735f43f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_example_flat.gin @@ -0,0 +1,149 @@ +#-*-Python-*- + +# NOTE: Should be run with >=10CPU for decent performance. + +import pybullet_envs.minitaur.agents.baseline_controller.torque_stance_leg_controller +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.mpc_locomotion_wrapper +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.sensors.camera_sensor +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.last_action_sensor +import pybullet_envs.minitaur.envs_v2.sensors.toe_position_sensor +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.envs_v2.tasks.terminal_conditions +import pybullet_envs.minitaur.envs_v2.utilities.noise_generators +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.robot_config + + +# Configure the dynamic robot + +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 4 # Control frequency will be 100 Hz + + +######################################## +# Configure the sensors +######################################## +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, 6283.18554688, 6283.18554688] + +# Add noise to the IMU sensor and toe position sensor +IMUNoise/noise_generators.NormalNoise.scale = (0.025, 0.025, 0.1, 0.1) +TOENoise/noise_generators.NormalNoise.scale = (0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005) +imu_sensor.IMUSensor.noise_generator = @IMUNoise/noise_generators.NormalNoise() +toe_position_sensor.ToePositionSensor.noise_generator = @TOENoise/noise_generators.NormalNoise() + +frontCamera/camera_sensor.CameraSensor.camera_translation_from_base = (0.197, 0.0, -0.115) +frontCamera/camera_sensor.CameraSensor.camera_rotation_from_base = (-0.4996018, 0.4999998, 0.4999998, 0.5003982) +frontCamera/camera_sensor.CameraSensor.parent_link_id = -1 +frontCamera/camera_sensor.CameraSensor.resolution = (32, 24) +frontCamera/camera_sensor.CameraSensor.sensor_latency = 0.03 +frontCamera/camera_sensor.CameraSensor.name = "frontCam" +frontCamera/camera_sensor.CameraSensor.fov_degree = 75 +frontCamera/camera_sensor.CameraSensor.camera_mode = %sim_camera.CameraMode.DEPTH +frontCamera/camera_sensor.CameraSensor.camera_update_frequency_hz = 30.0 + +rearCamera/camera_sensor.CameraSensor.camera_translation_from_base = (-0.092, 0.0, -0.105) +rearCamera/camera_sensor.CameraSensor.camera_rotation_from_base = (-0.4996018, 0.4999998, 0.4999998, 0.5003982) +rearCamera/camera_sensor.CameraSensor.parent_link_id = -1 +rearCamera/camera_sensor.CameraSensor.resolution = (32, 24) +rearCamera/camera_sensor.CameraSensor.sensor_latency = 0.03 +rearCamera/camera_sensor.CameraSensor.name = "rearCam" +rearCamera/camera_sensor.CameraSensor.fov_degree = 75 +rearCamera/camera_sensor.CameraSensor.camera_mode = %sim_camera.CameraMode.DEPTH +rearCamera/camera_sensor.CameraSensor.camera_update_frequency_hz = 30.0 + +sensors = [@imu_sensor.IMUSensor(), @last_action_sensor.LastActionSensor(), @toe_position_sensor.ToePositionSensor(), @frontCamera/camera_sensor.CameraSensor(), @rearCamera/camera_sensor.CameraSensor()] +laikago_v2.Laikago.sensors = %sensors + + +######################################## +# Specify the motor model and its parameters +######################################## +LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS = 6.28318548203 +LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS = -6.28318548203 +laikago/robot_config.MotorLimits.angle_lower_limits = %LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS +laikago/robot_config.MotorLimits.angle_upper_limits = %LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS +laikago/robot_config.MotorLimits.torque_lower_limits = -30 +laikago/robot_config.MotorLimits.torque_upper_limits = 30 +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.HYBRID +laikago_v2.Laikago.motor_model_class = @hybrid_motor_model.HybridMotorModel +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago +hybrid_motor_model.HybridMotorModel.kp = 250 +hybrid_motor_model.HybridMotorModel.kd = (0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0) + + +######################################## +# Setup the terrain randomization and simulation parameters +######################################## + +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT + + +######################################## +# Setup the task and terminal condition parameters +######################################## +terminal_conditions.maxstep_terminal_condition.max_step = 2000 +terminal_conditions.default_terminal_condition_for_laikago_v2.max_roll = 0.25 +terminal_conditions.default_terminal_condition_for_laikago_v2.max_pitch = 1.0 +terminal_conditions.default_terminal_condition_for_laikago_v2.min_height = 0.15 +terminal_conditions.default_terminal_condition_for_laikago_v2.enforce_foot_contacts = True + +# Setup the terminal condition +terminal_conditions.logical_any_terminal_condition.conditions = [ + @terminal_conditions.default_terminal_condition_for_laikago_v2, + @terminal_conditions.maxstep_terminal_condition, + ] + +simple_locomotion_task.SimpleForwardTask.terminal_condition = @terminal_conditions.logical_any_terminal_condition + +env_loader.load.wrapper_classes = [ + @mpc_locomotion_wrapper.MPCLocomotionWrapper, +] + +######################################## +# Configure the MPC-related parameters +######################################## +torque_stance_leg_controller.TorqueStanceLegController.qp_weights = (5, 5, 0.2, 0, 0, 10, 0.5, 0.5, 0.2, 0.2, 0.2, 0.1, 0) +torque_stance_leg_controller.TorqueStanceLegController.body_inertia = (0.183375, 0, 0, 0, 0.6267, 0, 0, 0, 0.636175) +torque_stance_leg_controller.TorqueStanceLegController.friction_coeffs = (0.45, 0.45, 0.45, 0.45) + +######################################## +# Configure the foothold wrapper parameters and action space +######################################## +mpc_locomotion_wrapper.MPCLocomotionWrapper.foot_friction_coeff = 1.0 +mpc_locomotion_wrapper.MPCLocomotionWrapper.locomotion_gait = %mpc_locomotion_wrapper.Gait.TROT +mpc_locomotion_wrapper.MPCLocomotionWrapper.control_frequency=20 +mpc_locomotion_wrapper.MPCLocomotionWrapper.target_horizontal_com_velocity_heuristic = @mpc_locomotion_wrapper.InverseRaibertTargetHorizontalComVelocityHeuristic() +mpc_locomotion_wrapper.InverseRaibertTargetHorizontalComVelocityHeuristic.gains = (-0.25, -0.1) + +mpc_locomotion_wrapper.MPCLocomotionWrapper.swing_target_action_range = ((-0.05, -0.05, -0.03), (0.1, 0.05, 0.03)) +mpc_locomotion_wrapper.MPCLocomotionWrapper.pitch_action_range = (-0.2, 0.2) +mpc_locomotion_wrapper.MPCLocomotionWrapper.roll_action_range = (-0.05, 0.05) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_velocity_action_range = ((-0.05, -0.05), (0.05, 0.05)) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_twist_action_range = (-0.2, 0.2) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_height_action_range = (0.42, 0.48) +mpc_locomotion_wrapper.MPCLocomotionWrapper.swing_clearance_action_range = (0.05, 0.1) + +imu_based_com_velocity_estimator.IMUBasedCOMVelocityEstimator.use_sensor_interface = False + + diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_stepstone.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_stepstone.gin new file mode 100644 index 000000000..2644d3b7b --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_mpc_stepstone.gin @@ -0,0 +1,142 @@ +#-*-Python-*- + +# NOTE: Should be run with >=10CPU for decent performance. + +import pybullet_envs.minitaur.agents.baseline_controller.torque_stance_leg_controller +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.mpc_locomotion_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.random_stepstone_scene +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.last_action_sensor +import pybullet_envs.minitaur.envs_v2.sensors.toe_position_sensor +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.envs_v2.tasks.terminal_conditions +import pybullet_envs.minitaur.envs_v2.utilities.noise_generators +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.robot_config + + +# Configure the dynamic robot + +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 4 # Control frequency will be 100 Hz + + +######################################## +# Configure the sensors +######################################## +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, 6283.18554688, 6283.18554688] + +# Add noise to the IMU sensor and toe position sensor +# IMUNoise/noise_generators.NormalNoise.scale = (0.025, 0.025, 0.1, 0.1) +# TOENoise/noise_generators.NormalNoise.scale = (0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005, 0.0025, 0.0025, 0.005) +# imu_sensor.IMUSensor.noise_generator = @IMUNoise/noise_generators.NormalNoise() +# toe_position_sensor.ToePositionSensor.noise_generator = @TOENoise/noise_generators.NormalNoise() + + +sensors = [@imu_sensor.IMUSensor(), @last_action_sensor.LastActionSensor(), @toe_position_sensor.ToePositionSensor()] +laikago_v2.Laikago.sensors = %sensors + + +######################################## +# Specify the motor model and its parameters +######################################## +LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS = 6.28318548203 +LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS = -6.28318548203 +laikago/robot_config.MotorLimits.angle_lower_limits = %LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS +laikago/robot_config.MotorLimits.angle_upper_limits = %LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS +laikago/robot_config.MotorLimits.torque_lower_limits = -30 +laikago/robot_config.MotorLimits.torque_upper_limits = 30 +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.HYBRID +laikago_v2.Laikago.motor_model_class = @hybrid_motor_model.HybridMotorModel +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago +hybrid_motor_model.HybridMotorModel.kp = 250 +hybrid_motor_model.HybridMotorModel.kd = (0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0) + + +######################################## +# Setup the terrain randomization and simulation parameters +######################################## + +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() +locomotion_gym_env.LocomotionGymEnv.scene = @random_stepstone_scene.RandomStepstoneScene() +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT + +random_stepstone_scene.RandomStepstoneScene.random_seed = 13 +random_stepstone_scene.RandomStepstoneScene.gap_length_lower_bound = 0.07 +random_stepstone_scene.RandomStepstoneScene.gap_length_upper_bound = 0.15 +random_stepstone_scene.RandomStepstoneScene.stone_width = 0.6 + +######################################## +# Setup the task and terminal condition parameters +######################################## +terminal_conditions.maxstep_terminal_condition.max_step = 2000 +terminal_conditions.default_terminal_condition_for_laikago_v2.max_roll = 0.25 +terminal_conditions.default_terminal_condition_for_laikago_v2.max_pitch = 1.0 +terminal_conditions.default_terminal_condition_for_laikago_v2.min_height = 0.15 +terminal_conditions.default_terminal_condition_for_laikago_v2.enforce_foot_contacts = True + +# Setup the terminal condition +terminal_conditions.logical_any_terminal_condition.conditions = [ + @terminal_conditions.default_terminal_condition_for_laikago_v2, + @terminal_conditions.maxstep_terminal_condition, + ] + + simple_locomotion_task.SimpleForwardTask.terminal_condition = @terminal_conditions.logical_any_terminal_condition +simple_locomotion_task.SimpleForwardTask.clip_velocity = 0.0015 + +time_ordered_buffer.TimeOrderedBuffer.error_on_duplicate_timestamp = False +time_ordered_buffer.TimeOrderedBuffer.replace_value_on_duplicate_timestamp = True +time_ordered_buffer.TimeOrderedBuffer.error_on_timestamp_reversal = False + + +observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper.observation_excluded = ('frontCam', 'rearCam') + +env_loader.load.wrapper_classes = [ + @mpc_locomotion_wrapper.MPCLocomotionWrapper, + @observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper, +] + +######################################## +# Configure the MPC-related parameters +######################################## +torque_stance_leg_controller.TorqueStanceLegController.qp_weights = (5, 5, 0.2, 0, 0, 10, 0.5, 0.5, 0.2, 0.2, 0.2, 0.1, 0) +torque_stance_leg_controller.TorqueStanceLegController.body_inertia = (0.183375, 0, 0, 0, 0.6267, 0, 0, 0, 0.636175) +torque_stance_leg_controller.TorqueStanceLegController.friction_coeffs = (0.45, 0.45, 0.45, 0.45) + +######################################## +# Configure the foothold wrapper parameters and action space +######################################## +mpc_locomotion_wrapper.MPCLocomotionWrapper.foot_friction_coeff = 1.0 +mpc_locomotion_wrapper.MPCLocomotionWrapper.locomotion_gait = %mpc_locomotion_wrapper.Gait.TROT +mpc_locomotion_wrapper.MPCLocomotionWrapper.control_frequency=20 +mpc_locomotion_wrapper.MPCLocomotionWrapper.target_horizontal_com_velocity_heuristic = @mpc_locomotion_wrapper.InverseRaibertTargetHorizontalComVelocityHeuristic() +mpc_locomotion_wrapper.InverseRaibertTargetHorizontalComVelocityHeuristic.gains = (-0.25, -0.1) + +mpc_locomotion_wrapper.MPCLocomotionWrapper.swing_target_action_range = ((-0.05, -0.05, -0.03), (0.1, 0.05, 0.03)) +mpc_locomotion_wrapper.MPCLocomotionWrapper.pitch_action_range = (-0.2, 0.2) +mpc_locomotion_wrapper.MPCLocomotionWrapper.roll_action_range = (-0.05, 0.05) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_velocity_action_range = ((-0.05, -0.05), (0.05, 0.05)) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_twist_action_range = (-0.2, 0.2) +mpc_locomotion_wrapper.MPCLocomotionWrapper.base_height_action_range = (0.42, 0.48) +mpc_locomotion_wrapper.MPCLocomotionWrapper.swing_clearance_action_range = (0.05, 0.1) + +imu_based_com_velocity_estimator.IMUBasedCOMVelocityEstimator.use_sensor_interface = False + diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_reactive.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_reactive.gin new file mode 100644 index 000000000..b4d6f8ba0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_reactive.gin @@ -0,0 +1,133 @@ +#-*-Python-*- + +# NOTE: Should be run with >=10CPU for decent performance. + +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.simple_openloop +import pybullet_envs.minitaur.envs_v2.env_wrappers.trajectory_generator_wrapper_env + + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.robots.laikago + +URDF_ROOT = "urdf/" +ABDUCTION_P_GAIN = 220.0 +ABDUCTION_D_GAIN = 0.3 +HIP_P_GAIN = 220.0 +HIP_D_GAIN = 2.0 +KNEE_P_GAIN = 220.0 +KNEE_D_GAIN = 2.0 + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.sensors.robot_sensors + +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 +NUM_MOTORS = 12 +NOISY_READING = True + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +robot_sensors.IMUSensor.channels = ["R", "P", "dR", "dP"] +robot_sensors.IMUSensor.noisy_reading = %NOISY_READING +robot_sensors.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +robot_sensors.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +robot_sensors.MotorAngleSensor.num_motors = %NUM_MOTORS +robot_sensors.MotorAngleSensor.noisy_reading = %NOISY_READING +robot_sensors.MotorAngleSensor.lower_bound = -6.28318548203 +robot_sensors.MotorAngleSensor.upper_bound = 6.28318548203 + +sensors = [@robot_sensors.IMUSensor(), @robot_sensors.MotorAngleSensor()] + +Act0/locomotion_gym_config.ScalarField.name = "motor_angle_0" +Act0/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act0/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act1/locomotion_gym_config.ScalarField.name = "motor_angle_1" +Act1/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act1/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act2/locomotion_gym_config.ScalarField.name = "motor_angle_2" +Act2/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act2/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act3/locomotion_gym_config.ScalarField.name = "motor_angle_3" +Act3/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act3/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act4/locomotion_gym_config.ScalarField.name = "motor_angle_4" +Act4/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act4/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act5/locomotion_gym_config.ScalarField.name = "motor_angle_5" +Act5/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act5/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act6/locomotion_gym_config.ScalarField.name = "motor_angle_6" +Act6/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act6/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act7/locomotion_gym_config.ScalarField.name = "motor_angle_7" +Act7/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act7/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act8/locomotion_gym_config.ScalarField.name = "motor_angle_8" +Act8/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act8/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act9/locomotion_gym_config.ScalarField.name = "motor_angle_9" +Act9/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act9/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act10/locomotion_gym_config.ScalarField.name = "motor_angle_10" +Act10/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act10/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act11/locomotion_gym_config.ScalarField.name = "motor_angle_11" +Act11/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act11/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND + + +locomotion_gym_config.LocomotionGymConfig.actions = [ + @Act0/locomotion_gym_config.ScalarField(), + @Act1/locomotion_gym_config.ScalarField(), + @Act2/locomotion_gym_config.ScalarField(), + @Act3/locomotion_gym_config.ScalarField(), + @Act4/locomotion_gym_config.ScalarField(), + @Act5/locomotion_gym_config.ScalarField(), + @Act6/locomotion_gym_config.ScalarField(), + @Act7/locomotion_gym_config.ScalarField(), + @Act8/locomotion_gym_config.ScalarField(), + @Act9/locomotion_gym_config.ScalarField(), + @Act10/locomotion_gym_config.ScalarField(), + @Act11/locomotion_gym_config.ScalarField()] + + + +laikago.Laikago.urdf_root = %URDF_ROOT +laikago.Laikago.time_step = %SIM_TIME_STEP +laikago.Laikago.action_repeat = %NUM_ACTION_REPEAT +laikago.Laikago.self_collision_enabled = False +laikago.Laikago.control_latency = 0.002 +laikago.Laikago.pd_latency = 0.0 +laikago.Laikago.motor_kp = [%ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN] +laikago.Laikago.motor_kd = [%ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN] +laikago.Laikago.sensors = %sensors + +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago.Laikago +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() + + + +trajectory_generator_wrapper_env.TrajectoryGeneratorWrapperEnv.trajectory_generator = @simple_openloop.LaikagoPoseOffsetGenerator() +env_loader.load.wrapper_classes = [ + @observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper, + @trajectory_generator_wrapper_env.TrajectoryGeneratorWrapperEnv] diff --git a/examples/pybullet/gym/pybullet_data/configs/laikago_walk_static_gait.gin b/examples/pybullet/gym/pybullet_data/configs/laikago_walk_static_gait.gin new file mode 100644 index 000000000..2b6cd680d --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/laikago_walk_static_gait.gin @@ -0,0 +1,65 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.simple_openloop +import pybullet_envs.minitaur.envs_v2.env_wrappers.trajectory_generator_wrapper_env +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.robot_config + + +# Specify the gym env parameters +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 4 +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + +# Specify the world. +URDF_ROOT = "" +scene_base.SceneBase.data_root = %URDF_ROOT +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() + +# Specify the task. +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() + +# Specify the sensors. Sensors determine the observation space. +# Sensors can either be mounted on robots (see below), or passed to envs +# i.e. like ambient sensors, or provided by tasks (task specific measures). +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE, +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +# We use the default confirugration for MotorAngleSensor, which reads limits from the robot. +SENSORS = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor()] + +# Specify the motor limits, and motor control mode. +LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS = 6.28318548203 +LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS = -6.28318548203 +laikago/robot_config.MotorLimits.angle_lower_limits = %LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS +laikago/robot_config.MotorLimits.angle_upper_limits = %LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.POSITION +laikago_v2.Laikago.motor_model_class = @hybrid_motor_model.HybridMotorModel +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago + +# Specify the motor model parameters. Notice that we don't need to specify the control mode or motor limits here. +hybrid_motor_model.HybridMotorModel.kp = 2400 +hybrid_motor_model.HybridMotorModel.kd = 5 + +# Finally, mount sensors specified above to the Laikago. +laikago_v2.Laikago.sensors = %SENSORS diff --git a/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_config.gin b/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_config.gin new file mode 100644 index 000000000..4b268980d --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_config.gin @@ -0,0 +1,48 @@ +#-*-Python-*- + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.robots.minitaur_motor_model_v2 +import pybullet_envs.minitaur.robots.minitaur_v2 +import pybullet_envs.minitaur.robots.robot_config + + +UPPER_BOUND = 1.0 +LOWER_BOUND = -1.0 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 20 +NUM_MOTORS = 8 +NOISY_READING = True +SENSOR_LATENCY = 0.02 + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +robot_config.MotorLimits.angle_lower_limits = %LOWER_BOUND +robot_config.MotorLimits.angle_upper_limits = %UPPER_BOUND + +minitaur_v2.Minitaur.motor_control_mode = %robot_config.MotorControlMode.POSITION +minitaur_v2.Minitaur.motor_limits = @robot_config.MotorLimits() +minitaur_v2.Minitaur.motor_model_class = @minitaur_motor_model_v2.MinitaurMotorModel +minitaur_motor_model_v2.MinitaurMotorModel.pd_latency = 0.003 +minitaur_motor_model_v2.MinitaurMotorModel.kp = 1.0 +minitaur_motor_model_v2.MinitaurMotorModel.kd = 0.015 + +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE +] +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +motor_angle_sensor.MotorAngleSensor.sensor_latency = %SENSOR_LATENCY +motor_angle_sensor.MotorAngleSensor.lower_bound = -6.28318548203 +motor_angle_sensor.MotorAngleSensor.upper_bound = 6.28318548203 +minitaur_v2.Minitaur.sensors = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor()] diff --git a/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_env.gin b/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_env.gin new file mode 100644 index 000000000..4b5140f0f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs/minitaur_gym_env.gin @@ -0,0 +1,63 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.robots.minitaur_v2 +import pybullet_envs.minitaur.robots.robot_config +import pybullet_envs.minitaur.robots.robot_urdf_loader + +URDF_ROOT = "urdf/" +#-*-Python-*- + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.robots.minitaur_motor_model_v2 +import pybullet_envs.minitaur.robots.minitaur_v2 +import pybullet_envs.minitaur.robots.robot_config + + +UPPER_BOUND = 1.0 +LOWER_BOUND = -1.0 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 20 +NUM_MOTORS = 8 +NOISY_READING = True +SENSOR_LATENCY = 0.02 + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +robot_config.MotorLimits.angle_lower_limits = %LOWER_BOUND +robot_config.MotorLimits.angle_upper_limits = %UPPER_BOUND + +minitaur_v2.Minitaur.motor_control_mode = %robot_config.MotorControlMode.POSITION +minitaur_v2.Minitaur.motor_limits = @robot_config.MotorLimits() +minitaur_v2.Minitaur.motor_model_class = @minitaur_motor_model_v2.MinitaurMotorModel +minitaur_motor_model_v2.MinitaurMotorModel.pd_latency = 0.003 +minitaur_motor_model_v2.MinitaurMotorModel.kp = 1.0 +minitaur_motor_model_v2.MinitaurMotorModel.kd = 0.015 + +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE +] +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +motor_angle_sensor.MotorAngleSensor.sensor_latency = %SENSOR_LATENCY +motor_angle_sensor.MotorAngleSensor.lower_bound = -6.28318548203 +motor_angle_sensor.MotorAngleSensor.upper_bound = 6.28318548203 +minitaur_v2.Minitaur.sensors = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor()] + + +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() +locomotion_gym_env.LocomotionGymEnv.robot_class = @minitaur_v2.Minitaur +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() + diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_reactive.gin b/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_reactive.gin new file mode 100644 index 000000000..030bf4d8f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_reactive.gin @@ -0,0 +1,80 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.simple_openloop +import pybullet_envs.minitaur.envs_v2.env_wrappers.trajectory_generator_wrapper_env +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.sensors.accelerometer_sensor +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.envs_v2.tasks.terminal_conditions +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.time_ordered_buffer + + +# Specify the gym env parameters +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + + +# Specify the robot +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago +LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS = 6.28318548203 +LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS = -6.28318548203 +laikago/robot_config.MotorLimits.angle_lower_limits = %LAIKAGO_MOTOR_ANGLE_LOWER_LIMITS +laikago/robot_config.MotorLimits.angle_upper_limits = %LAIKAGO_MOTOR_ANGLE_UPPER_LIMITS +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.POSITION + +# Specify the motor model parameters. Notice that we don't need to specify the control mode +# and motor limits here, as they will be passed from the robot interface. +hybrid_motor_model.HybridMotorModel.kp = 220 +hybrid_motor_model.HybridMotorModel.kd = (0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0, 0.3, 2.0, 2.0) + +# This will make sure the hybrid motor model does not throw error during reset, when the timestamp is alwasy zero. +time_ordered_buffer.TimeOrderedBuffer.error_on_duplicate_timestamp = False +time_ordered_buffer.TimeOrderedBuffer.replace_value_on_duplicate_timestamp = True + +# Add the sensors +laikago_v2.Laikago.sensors = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor(), @accelerometer_sensor.AccelerometerSensor()] +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE, +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +# Specify the scene +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() +simple_scene.SimpleScene.data_root = "third_party/bullet/data" + +# Specify the task. +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() +simple_locomotion_task.SimpleForwardTask.terminal_condition = @terminal_conditions.default_terminal_condition_for_laikago + +accelerometer_sensor.AccelerometerSensor.lower_bound = [-50.0, -50.0, -50.0] +accelerometer_sensor.AccelerometerSensor.upper_bound = [50.0, 50.0, 50.0] +accelerometer_sensor.AccelerometerSensor.sensor_latency = 0.01 +imu_sensor.IMUSensor.sensor_latency = 0.1 + + +# Define the wrappers needed +env_loader.load.wrapper_classes = [ + @trajectory_generator_wrapper_env.TrajectoryGeneratorWrapperEnv, +] +trajectory_generator_wrapper_env.TrajectoryGeneratorWrapperEnv.trajectory_generator = @simple_openloop.LaikagoPoseOffsetGenerator() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_with_imu.gin b/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_with_imu.gin new file mode 100644 index 000000000..03d7c3308 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/base/laikago_with_imu.gin @@ -0,0 +1,66 @@ +#import pybullet_data +#MYPATH = pybullet_data.getDataPath()+'/configs_v2/robots/laikago.gin' +#MYPATH = 'D:/dev/bullet3/examples/pybullet\gym/pybullet_data/configs_v2/robots/laikago.gin' +#include '$MYPATH/ambiguous.gin' + +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.scene_base +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.robot_config + +URDF_ROOT = "" +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE, +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +# We use the default confirugration for MotorAngleSensor, which reads limits from the robot. +SENSORS = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor()] +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + +# Specify the scene. +scene_base.SceneBase.data_root = %URDF_ROOT +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() + +# Specify the motor limits, and motor control mode. +laikago/robot_config.MotorLimits.angle_lower_limits = %LOWER_BOUND +laikago/robot_config.MotorLimits.angle_upper_limits = %UPPER_BOUND +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.POSITION +laikago_v2.Laikago.motor_model_class = @hybrid_motor_model.HybridMotorModel +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago + +# Specify the motor model parameters. Notice that we don't need to specify the control mode or motor limits here. +hybrid_motor_model.HybridMotorModel.kp = 220 +hybrid_motor_model.HybridMotorModel.kd = 2 + +laikago_v2.Laikago.sensors = %SENSORS + + +laikago_v2.Laikago.sensors = [@imu_sensor.IMUSensor()] diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/base/mini_cheetah_with_imu.gin b/examples/pybullet/gym/pybullet_data/configs_v2/base/mini_cheetah_with_imu.gin new file mode 100644 index 000000000..d7ebe63f7 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/base/mini_cheetah_with_imu.gin @@ -0,0 +1,9 @@ +import pybullet_data as pd + +include pd.getDataPath()+'/configs_v2/robots/mini_cheetah.gin' +include pd.getDataPath()+'/configs_v2/sensors/imu.gin' + +#include 'robotics/reinforcement_learning/minitaur/envs_v2/configs_v2/robots/mini_cheetah.gin' +#include 'robotics/reinforcement_learning/minitaur/envs_v2/configs_v2/sensors/imu.gin' + +mini_cheetah.MiniCheetah.sensors = [@robot_sensors.IMUSensor()] diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/robots/laikago.gin b/examples/pybullet/gym/pybullet_data/configs_v2/robots/laikago.gin new file mode 100644 index 000000000..a639c83c9 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/robots/laikago.gin @@ -0,0 +1,58 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.scene_base +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene +import pybullet_envs.minitaur.envs_v2.sensors.imu_sensor +import pybullet_envs.minitaur.envs_v2.sensors.motor_angle_sensor +import pybullet_envs.minitaur.robots.hybrid_motor_model +import pybullet_envs.minitaur.robots.laikago_v2 +import pybullet_envs.minitaur.robots.robot_config + +URDF_ROOT = "" +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +imu_sensor.IMUSensor.channels = [ + %imu_sensor.IMUChannel.ROLL, + %imu_sensor.IMUChannel.PITCH, + %imu_sensor.IMUChannel.ROLL_RATE, + %imu_sensor.IMUChannel.PITCH_RATE, +] + +imu_sensor.IMUSensor.lower_bound = [-6.28318548203, -6.28318548203, + -6283.18554688, -6283.18554688] +imu_sensor.IMUSensor.upper_bound = [6.28318548203, 6.28318548203, + 6283.18554688, 6283.18554688] + +# We use the default confirugration for MotorAngleSensor, which reads limits from the robot. +SENSORS = [@imu_sensor.IMUSensor(), @motor_angle_sensor.MotorAngleSensor()] +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + +# Specify the scene. +scene_base.SceneBase.data_root = %URDF_ROOT +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() + +# Specify the motor limits, and motor control mode. +laikago/robot_config.MotorLimits.angle_lower_limits = %LOWER_BOUND +laikago/robot_config.MotorLimits.angle_upper_limits = %UPPER_BOUND +laikago_v2.Laikago.motor_limits = @laikago/robot_config.MotorLimits() +laikago_v2.Laikago.motor_control_mode = %robot_config.MotorControlMode.POSITION +laikago_v2.Laikago.motor_model_class = @hybrid_motor_model.HybridMotorModel +locomotion_gym_env.LocomotionGymEnv.robot_class = @laikago_v2.Laikago + +# Specify the motor model parameters. Notice that we don't need to specify the control mode or motor limits here. +hybrid_motor_model.HybridMotorModel.kp = 220 +hybrid_motor_model.HybridMotorModel.kd = 2 + +laikago_v2.Laikago.sensors = %SENSORS diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/robots/mini_cheetah.gin b/examples/pybullet/gym/pybullet_data/configs_v2/robots/mini_cheetah.gin new file mode 100644 index 000000000..7433da2f8 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/robots/mini_cheetah.gin @@ -0,0 +1,98 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.robots.mini_cheetah +import pybullet_data as pd + +URDF_ROOT = pd.getDataPath()+"/urdf/" + +ABDUCTION_P_GAIN = 100.0 +ABDUCTION_D_GAIN = 1.0 +HIP_P_GAIN = 30 +HIP_D_GAIN = 2.0 +KNEE_P_GAIN = 50 +KNEE_D_GAIN = 2.0 + + +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 2 +NUM_MOTORS = 12 +NOISY_READING = True + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() + +Act0/locomotion_gym_config.ScalarField.name = "motor_angle_0" +Act0/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act0/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act1/locomotion_gym_config.ScalarField.name = "motor_angle_1" +Act1/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act1/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act2/locomotion_gym_config.ScalarField.name = "motor_angle_2" +Act2/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act2/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act3/locomotion_gym_config.ScalarField.name = "motor_angle_3" +Act3/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act3/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act4/locomotion_gym_config.ScalarField.name = "motor_angle_4" +Act4/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act4/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act5/locomotion_gym_config.ScalarField.name = "motor_angle_5" +Act5/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act5/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act6/locomotion_gym_config.ScalarField.name = "motor_angle_6" +Act6/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act6/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act7/locomotion_gym_config.ScalarField.name = "motor_angle_7" +Act7/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act7/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act8/locomotion_gym_config.ScalarField.name = "motor_angle_8" +Act8/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act8/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act9/locomotion_gym_config.ScalarField.name = "motor_angle_9" +Act9/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act9/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act10/locomotion_gym_config.ScalarField.name = "motor_angle_10" +Act10/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act10/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND +Act11/locomotion_gym_config.ScalarField.name = "motor_angle_11" +Act11/locomotion_gym_config.ScalarField.upper_bound = %UPPER_BOUND +Act11/locomotion_gym_config.ScalarField.lower_bound = %LOWER_BOUND + + +locomotion_gym_config.LocomotionGymConfig.actions = [ + @Act0/locomotion_gym_config.ScalarField(), + @Act1/locomotion_gym_config.ScalarField(), + @Act2/locomotion_gym_config.ScalarField(), + @Act3/locomotion_gym_config.ScalarField(), + @Act4/locomotion_gym_config.ScalarField(), + @Act5/locomotion_gym_config.ScalarField(), + @Act6/locomotion_gym_config.ScalarField(), + @Act7/locomotion_gym_config.ScalarField(), + @Act8/locomotion_gym_config.ScalarField(), + @Act9/locomotion_gym_config.ScalarField(), + @Act10/locomotion_gym_config.ScalarField(), + @Act11/locomotion_gym_config.ScalarField()] + +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + +mini_cheetah.MiniCheetah.urdf_root = %URDF_ROOT +mini_cheetah.MiniCheetah.time_step = %SIM_TIME_STEP +mini_cheetah.MiniCheetah.action_repeat = %NUM_ACTION_REPEAT +mini_cheetah.MiniCheetah.self_collision_enabled = False +mini_cheetah.MiniCheetah.control_latency = 0.002 +mini_cheetah.MiniCheetah.pd_latency = 0.0 +mini_cheetah.MiniCheetah.motor_kp = [%ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN, + %ABDUCTION_P_GAIN, %HIP_P_GAIN, %KNEE_P_GAIN] +mini_cheetah.MiniCheetah.motor_kd = [%ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN, + %ABDUCTION_D_GAIN, %HIP_D_GAIN, %KNEE_D_GAIN] + +locomotion_gym_env.LocomotionGymEnv.robot_class = @mini_cheetah.MiniCheetah + diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/robots/minitaur.gin b/examples/pybullet/gym/pybullet_data/configs_v2/robots/minitaur.gin new file mode 100644 index 000000000..091483380 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/robots/minitaur.gin @@ -0,0 +1,31 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_config +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.robots.minitaur_motor_model_v2 +import pybullet_envs.minitaur.robots.minitaur_v2 + +URDF_ROOT = "robotics/reinforcement_learning/minitaur/robots/data/urdf/" + +UPPER_BOUND = 6.28318548203 +LOWER_BOUND = -6.28318548203 +SIM_TIME_STEP = 0.001 +NUM_ACTION_REPEAT = 6 +NUM_MOTORS = 8 +NOISY_READING = True + +locomotion_gym_config.SimulationParameters.sim_time_step_s = %SIM_TIME_STEP +locomotion_gym_config.SimulationParameters.num_action_repeat = %NUM_ACTION_REPEAT +locomotion_gym_config.SimulationParameters.enable_rendering = False +locomotion_gym_config.LocomotionGymConfig.simulation_parameters = @locomotion_gym_config.SimulationParameters() +locomotion_gym_env.LocomotionGymEnv.gym_config = @locomotion_gym_config.LocomotionGymConfig() + +minitaur_v2.Minitaur.motor_control_mode = %robot_config.MotorControlMode.POSITION +minitaur_v2.Minitaur.motor_limits = @robot_config.MotorLimits() +minitaur_v2.Minitaur.motor_model_class = @minitaur_motor_model_v2.MinitaurMotorModel +minitaur_motor_model_v2.MinitaurMotorModel.pd_latency = 0.003 +minitaur_motor_model_v2.MinitaurMotorModel.kp = 1.0 +minitaur_motor_model_v2.MinitaurMotorModel.kd = 0.015 + +locomotion_gym_env.LocomotionGymEnv.robot_class = @minitaur_v2.Minitaur + +robot_config.MotorLimits.angle_lower_limits = %LOWER_BOUND +robot_config.MotorLimits.angle_upper_limits = %UPPER_BOUND diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/scenes/simple_scene.gin b/examples/pybullet/gym/pybullet_data/configs_v2/scenes/simple_scene.gin new file mode 100644 index 000000000..6cb12467a --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/scenes/simple_scene.gin @@ -0,0 +1,4 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.scenes.simple_scene + +locomotion_gym_env.LocomotionGymEnv.scene = @simple_scene.SimpleScene() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/scenes/stair_scene.gin b/examples/pybullet/gym/pybullet_data/configs_v2/scenes/stair_scene.gin new file mode 100644 index 000000000..134f5774f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/scenes/stair_scene.gin @@ -0,0 +1,3 @@ +import pybullet_envs.minitaur.envs_v2.scenes.stair_scene +# Specify the scene (overwrite the setting from laikago_reactive.gin) +locomotion_gym_env.LocomotionGymEnv.scene = @stair_scene.StairScene() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task.gin b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task.gin new file mode 100644 index 000000000..316431710 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task.gin @@ -0,0 +1,4 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task + +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_laikago.gin b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_laikago.gin new file mode 100644 index 000000000..59a50e4e7 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_laikago.gin @@ -0,0 +1,8 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task + +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() + +simple_locomotion_task.SimpleForwardTask.energy_penalty_coef = 0.002 +simple_locomotion_task.SimpleForwardTask.min_com_height = 0.3 +simple_locomotion_task.SimpleForwardTask.clip_velocity = 0.002 diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination.gin b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination.gin new file mode 100644 index 000000000..5fc8189ca --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination.gin @@ -0,0 +1,11 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.envs_v2.tasks.terminal_conditions + +terminal_conditions.maxstep_terminal_condition.max_step = 1500 +# Setup the terminal condition to not to terminate early when the robot falls. +terminal_conditions.logical_any_terminal_condition.conditions = [ + @terminal_conditions.maxstep_terminal_condition, + ] +simple_locomotion_task.SimpleForwardTask.terminal_condition = @terminal_conditions.logical_any_terminal_condition +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination_simplified.gin b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination_simplified.gin new file mode 100644 index 000000000..c7d9630d5 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/fwd_task_no_termination_simplified.gin @@ -0,0 +1,12 @@ +import pybullet_envs.minitaur.envs_v2.locomotion_gym_env +import pybullet_envs.minitaur.envs_v2.tasks.simple_locomotion_task +import pybullet_envs.minitaur.envs_v2.tasks.terminal_conditions + +terminal_conditions.maxstep_terminal_condition.max_step = 1500 +simple_locomotion_task.SimpleForwardTask.terminal_condition = @terminal_conditions.default_terminal_condition_for_laikago_v2 + +simple_locomotion_task.SimpleForwardTask.divide_with_dt = True +simple_locomotion_task.SimpleForwardTask.clip_velocity = 0.4 +simple_locomotion_task.SimpleForwardTask.energy_penalty_coef = 0 + +locomotion_gym_env.LocomotionGymEnv.task = @simple_locomotion_task.SimpleForwardTask() diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper.gin b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper.gin new file mode 100644 index 000000000..f9466a316 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper.gin @@ -0,0 +1,18 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.pmtg_wrapper_env + +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_enable = True +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_enable = 1 +pmtg_wrapper_env.PmtgWrapperEnv.intensity_upper_bound = 1.0 +pmtg_wrapper_env.PmtgWrapperEnv.max_delta_time = 4.0 +pmtg_wrapper_env.PmtgWrapperEnv.min_delta_time = 2.0 +pmtg_wrapper_env.PmtgWrapperEnv.residual_range = 0.35 +pmtg_wrapper_env.PmtgWrapperEnv.integrator_coupling_mode = "all coupled" +pmtg_wrapper_env.PmtgWrapperEnv.walk_height_coupling_mode = "all coupled" +pmtg_wrapper_env.PmtgWrapperEnv.variable_swing_stance_ratio = 1 +pmtg_wrapper_env.PmtgWrapperEnv.init_gait = "walk" + +env_loader.load.wrapper_classes = [ + @pmtg_wrapper_env.PmtgWrapperEnv, + @observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper] diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_dict.gin b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_dict.gin new file mode 100644 index 000000000..74251e31c --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_dict.gin @@ -0,0 +1,6 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.pmtg_wrapper_env + +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_enable = True + +env_loader.load.wrapper_classes = [@pmtg_wrapper_env.PmtgWrapperEnv] diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_laikago.gin b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_laikago.gin new file mode 100644 index 000000000..8f46b1591 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_laikago.gin @@ -0,0 +1,10 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.pmtg_wrapper_env + +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_enable = True +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_high_cut = 0.5 + +env_loader.load.wrapper_classes = [ + @pmtg_wrapper_env.PmtgWrapperEnv, + @observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper] diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_simplified_env.gin b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_simplified_env.gin new file mode 100644 index 000000000..f6736cb09 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/pmtg_wrapper_simplified_env.gin @@ -0,0 +1,18 @@ +import pybullet_envs.minitaur.envs_v2.env_loader +import pybullet_envs.minitaur.envs_v2.env_wrappers.observation_dictionary_to_array_wrapper +import pybullet_envs.minitaur.envs_v2.env_wrappers.pmtg_wrapper_env + + +pmtg_wrapper_env.PmtgWrapperEnv.init_leg_phase_offsets = [0, 0.5, 0.5, 0] +pmtg_wrapper_env.PmtgWrapperEnv.integrator_coupling_mode = 'all coupled' +pmtg_wrapper_env.PmtgWrapperEnv.intensity_upper_bound = 0.5 +pmtg_wrapper_env.PmtgWrapperEnv.max_delta_time = 4 +pmtg_wrapper_env.PmtgWrapperEnv.min_delta_time = 1 +pmtg_wrapper_env.PmtgWrapperEnv.residual_range = 0.2 +pmtg_wrapper_env.PmtgWrapperEnv.variable_swing_stance_ratio = False +pmtg_wrapper_env.PmtgWrapperEnv.walk_height_coupling_mode = 'null' +pmtg_wrapper_env.PmtgWrapperEnv.action_filter_high_cut = 0.5 + +env_loader.load.wrapper_classes = [ + @pmtg_wrapper_env.PmtgWrapperEnv, + @observation_dictionary_to_array_wrapper.ObservationDictionaryToArrayWrapper] diff --git a/examples/pybullet/gym/pybullet_data/testdata/__init__.py b/examples/pybullet/gym/pybullet_data/testdata/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git 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b/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py @@ -1 +0,0 @@ - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_height_estimator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_height_estimator.py new file mode 100644 index 000000000..3838c74aa --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_height_estimator.py @@ -0,0 +1,76 @@ +"""State estimator for robot height.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +from typing import Any, Sequence + +import gin + +from pybullet_envs.minitaur.agents.baseline_controller import state_estimator + + +@gin.configurable +class COMHeightEstimator(state_estimator.StateEstimatorBase): + """Estimate the CoM height using base orientation and local toe positions.""" + + def __init__( + self, + robot: Any, + com_estimate_leg_indices: Sequence[int] = (0, 1, 2, 3), + initial_com_height: float = 0.45, + ): + """Initializes the class. + + Args: + robot: A quadruped robot. + com_estimate_leg_indices: Leg indices used in estimating the CoM height. + initial_com_height: CoM height used during reset. + """ + self._robot = robot + self._com_estimate_leg_indices = com_estimate_leg_indices + self._initial_com_estimate_leg_indices = copy.copy(com_estimate_leg_indices) + self._initial_com_height = initial_com_height + self.reset(0) + + @property + def estimated_com_height(self): + return self._com_height + + def reset(self, current_time): + del current_time + self._com_height = self._initial_com_height + self._com_estimate_leg_indices = copy.copy( + self._initial_com_estimate_leg_indices) + + def update(self, current_time): + del current_time + local_toe_poses = self._robot.foot_positions( + position_in_world_frame=False) + # We rotate the local toe positions into the world orientation centered + # at the robot base to estimate the height of the robot. + world_toe_poses = [] + for toe_p in local_toe_poses: + world_toe_poses.append( + self._robot.pybullet_client.multiplyTransforms( + positionA=(0, 0, 0), + orientationA=self._robot.base_orientation_quaternion, + positionB=toe_p, + orientationB=(0, 0, 0, 1))[0]) + mean_height = 0.0 + num_toe_in_contact = 0 + for leg_id in self._com_estimate_leg_indices: + mean_height += world_toe_poses[leg_id][2] + num_toe_in_contact += 1 + mean_height /= num_toe_in_contact + self._com_height = abs(mean_height) + + @property + def com_estimate_leg_indices(self): + return self._com_estimate_leg_indices + + @com_estimate_leg_indices.setter + def com_estimate_leg_indices(self, leg_indices): + self._com_estimate_leg_indices = leg_indices diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_velocity_estimator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_velocity_estimator.py new file mode 100644 index 000000000..609f83271 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/com_velocity_estimator.py @@ -0,0 +1,83 @@ +"""State estimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import Any, Sequence + +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import state_estimator +from pybullet_envs.minitaur.robots.safety.python import moving_window_filter + +_DEFAULT_WINDOW_SIZE = 20 + + +@gin.configurable +class COMVelocityEstimator(state_estimator.StateEstimatorBase): + """Estimate the CoM velocity using on board sensors. + + + Requires knowledge about the base velocity in world frame, which for example + can be obtained from a MoCap system. This estimator will filter out the high + frequency noises in the velocity so the results can be used with controllers + reliably. + + """ + + def __init__( + self, + robot: Any, + window_size: int = _DEFAULT_WINDOW_SIZE, + ): + self._robot = robot + self._window_size = window_size + self.reset(0) + + @property + def com_velocity_body_yaw_aligned_frame(self) -> Sequence[float]: + """The base velocity projected in the body aligned inertial frame. + + The body aligned frame is a intertia frame that coincides with the body + frame, but has a zero relative velocity/angular velocity to the world frame. + + Returns: + The com velocity in body aligned frame. + """ + return self._com_velocity_body_yaw_aligned_frame + + @property + def com_velocity_world_frame(self) -> Sequence[float]: + return self._com_velocity_world_frame + + def reset(self, current_time): + del current_time + # We use a moving window filter to reduce the noise in velocity estimation. + self._velocity_filter_x = moving_window_filter.MovingWindowFilter( + window_size=self._window_size) + self._velocity_filter_y = moving_window_filter.MovingWindowFilter( + window_size=self._window_size) + self._velocity_filter_z = moving_window_filter.MovingWindowFilter( + window_size=self._window_size) + self._com_velocity_world_frame = np.array((0, 0, 0)) + self._com_velocity_body_yaw_aligned_frame = np.array((0, 0, 0)) + + def update(self, current_time): + del current_time + velocity = self._robot.base_velocity + + vx = self._velocity_filter_x.CalculateAverage(velocity[0]) + vy = self._velocity_filter_y.CalculateAverage(velocity[1]) + vz = self._velocity_filter_z.CalculateAverage(velocity[2]) + self._com_velocity_world_frame = np.array((vx, vy, vz)) + + base_orientation = self._robot.base_orientation_quaternion + _, inverse_rotation = self._robot.pybullet_client.invertTransform( + (0, 0, 0), base_orientation) + + self._com_velocity_body_yaw_aligned_frame, _ = ( + self._robot.pybullet_client.multiplyTransforms( + (0, 0, 0), inverse_rotation, self._com_velocity_world_frame, + (0, 0, 0, 1))) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/dummy_gait_generator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/dummy_gait_generator.py new file mode 100644 index 000000000..51747904f --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/dummy_gait_generator.py @@ -0,0 +1,87 @@ +"""A dummy gait generator module for storing gait patterns from higher-level controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import google_type_annotations +from __future__ import print_function + +import copy +from typing import Any, Sequence + +import gin + +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator + +LAIKAGO_STANDING = ( + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, +) + + +@gin.configurable +class DummyGaitGenerator(gait_generator.GaitGenerator): + """A module for storing quadruped gait patterns from high-level controller. + + This module stores the state for each leg of a quadruped robot. The data is + used by the stance leg controller to determine the appropriate contact forces. + A high-level controller, such as a neural network policy, can be used to + control the gait pattern and set corresponding leg states. + """ + + def __init__( + self, + robot: Any, + initial_leg_state: Sequence[gait_generator.LegState] = LAIKAGO_STANDING, + ): + """Initializes the class. + + Args: + robot: A quadruped robot. + initial_leg_state: The desired initial swing/stance state of legs indexed + by their id. + """ + self._robot = robot + if len(initial_leg_state) != len( + list(self._robot.urdf_loader.get_end_effector_id_dict().values())): + raise ValueError( + "The number of leg states should be the same of number of legs.") + self._initial_leg_state = initial_leg_state + self._leg_state = list(initial_leg_state) + self._desired_leg_state = list(initial_leg_state) + + self.reset(0) + + def reset(self, current_time): + del current_time + self._leg_state = list(self._initial_leg_state) + self._desired_leg_state = list(self._initial_leg_state) + + @property + def desired_leg_state(self) -> Sequence[gait_generator.LegState]: + """The desired leg SWING/STANCE states. + + Returns: + The SWING/STANCE states for all legs. + + """ + return self._desired_leg_state + + @desired_leg_state.setter + def desired_leg_state(self, state): + self._desired_leg_state = copy.deepcopy(state) + + @property + def leg_state(self) -> Sequence[gait_generator.LegState]: + """The leg state after considering contact with ground. + + Returns: + The actual state of each leg after accounting for contacts. + """ + return self._leg_state + + @leg_state.setter + def leg_state(self, state): + self._leg_state = copy.deepcopy(state) + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/foot_stepper.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/foot_stepper.py new file mode 100644 index 000000000..8a79a236f --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/foot_stepper.py @@ -0,0 +1,199 @@ +# Lint as: python3 +"""A state machine that steps each foot for a static gait. Experimental code.""" + +import copy +import math + +import numpy as np + + +class StepInput(object): + + def __init__(self): + self.base_com_pos = np.array([0, 0, 0]) + self.base_com_orn = np.array([0, 0, 0, 1]) + self.toe_pos_world = np.array([0, 0, 0] * 4) + self.new_pos_world = np.array([0, 0, 0]) + + +class StepOutput(object): + + def __init__(self, new_toe_pos_world): + self.new_toe_pos_world = new_toe_pos_world + + +class FootStepper(object): + """This class computes desired foot placement for a quadruped robot.""" + + def __init__(self, bullet_client, toe_ids, toe_pos_local_ref): + self.bullet_client = bullet_client + self.state_time = 0. + self.toe_ids = toe_ids + self.toe_pos_local_ref = toe_pos_local_ref + self.sphere_uid = self.bullet_client.loadURDF( + "sphere_small.urdf", [0, 0, 0], useFixedBase=True) + self.is_far = True + self.max_shift = 0.0008 + self.far_bound = 0.005 + self.close_bound = 0.03 + + self.move_swing_foot = False + self.amp = 0.2 + alpha = 1 + + # Loads/draws spheres for debugging purpose. The spheres visualize the + # target COM, the current COM and the target foothold location. + self.sphere_uid_centroid = self.bullet_client.loadURDF( + "sphere_small.urdf", [0, 0, 0], useFixedBase=True) + self.bullet_client.changeVisualShape( + self.sphere_uid_centroid, -1, rgbaColor=[1, 1, 0, alpha]) + + # Disable collision since visualization spheres should not collide with the + # robot. + self.bullet_client.setCollisionFilterGroupMask(self.sphere_uid_centroid, -1, + 0, 0) + + self.sphere_uid_com = self.bullet_client.loadURDF( + "sphere_small.urdf", [0, 0, 0], useFixedBase=True) + self.bullet_client.changeVisualShape( + self.sphere_uid_com, -1, rgbaColor=[1, 0, 1, alpha]) + self.bullet_client.setCollisionFilterGroupMask(self.sphere_uid_com, -1, 0, + 0) + + self.bullet_client.setCollisionFilterGroupMask(self.sphere_uid, -1, 0, 0) + self.feetindices = [1, 3, 0, 2] + self.swing_foot_index1 = 0 + self.swing_foot_index = self.feetindices[self.swing_foot_index1] + self.colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]] + self.support_vertices = [[1, 2, 3], [0, 2, 3], [0, 1, 3], [0, 1, 2]] + self.local_diff_y_threshold = 0.05 + self.local_diff_y = 100 + self.is_far = True + self.get_reference_pos_swing_foot() + + def next_foot(self): + self.swing_foot_index1 = (self.swing_foot_index1 + 1) % 4 + self.swing_foot_index = self.feetindices[self.swing_foot_index1] + + def swing_foot(self): + self.move_swing_foot = True + + def get_reference_pos_swing_foot(self): + self.new_pos_local = np.array( + self.toe_pos_local_ref[self.swing_foot_index]) + return self.new_pos_local + + def set_reference_pos_swing_foot(self, new_pos_local): + self.new_pos_local = new_pos_local + + def is_com_stable(self): + ld2 = self.local_diff_y * self.local_diff_y + yaw_ok = ld2 < (self.local_diff_y_threshold * self.local_diff_y_threshold) + com_ok = not self.is_far + return com_ok and yaw_ok + + def update(self, step_input): + """Updates the state machine and toe movements per state.""" + base_com_pos = step_input.base_com_pos + base_com_orn = step_input.base_com_orn + base_com_pos_inv, base_com_orn_inv = self.bullet_client.invertTransform( + base_com_pos, base_com_orn) + + dt = step_input.dt + self.bullet_client.resetBasePositionAndOrientation(self.sphere_uid, + step_input.new_pos_world, + [0, 0, 0, 1]) + self.bullet_client.changeVisualShape( + self.sphere_uid, -1, rgbaColor=self.colors[self.swing_foot_index]) + + all_toes_pos_locals = [] + for toe_pos_world in step_input.toe_pos_world: + toe_pos_local, _ = self.bullet_client.multiplyTransforms( + base_com_pos_inv, base_com_orn_inv, toe_pos_world, [0, 0, 0, 1]) + all_toes_pos_locals.append(toe_pos_local) + all_toes_pos_locals = np.array(all_toes_pos_locals) + centroid_world = np.zeros(3) + for v in self.support_vertices[self.swing_foot_index]: + vtx_pos_world = step_input.toe_pos_world[v] + centroid_world += vtx_pos_world + centroid_world /= 3. + + sphere_z_offset = 0.05 + self.diff_world = base_com_pos - centroid_world + self.diff_world[2] = 0. + self.bullet_client.resetBasePositionAndOrientation(self.sphere_uid_centroid, + centroid_world, + [0, 0, 0, 1]) + self.bullet_client.resetBasePositionAndOrientation( + self.sphere_uid_com, + [base_com_pos[0], base_com_pos[1], sphere_z_offset], [0, 0, 0, 1]) + + l = np.linalg.norm(self.diff_world) + if self.is_far: + bound = self.far_bound + else: + bound = self.close_bound + + if l > bound: + self.diff_world *= self.max_shift * 0.5 / l + if not self.is_far: + self.is_far = True + else: + if self.is_far: + self.is_far = False + + if not self.is_far: + self.diff_world = np.zeros(3) + for i in range(len(self.toe_pos_local_ref)): + toe = self.toe_pos_local_ref[i] + toe = [ + toe[0] + self.diff_world[0], toe[1] + self.diff_world[1], + toe[2] + self.diff_world[2] + ] + self.toe_pos_local_ref[i] = toe + + self.local_diff_y = self.toe_pos_local_ref[0][ + 1] + self.toe_pos_local_ref[1][1] - self.toe_pos_local_ref[ + 2][1] - self.toe_pos_local_ref[3][1] + + self.yaw = 0 + if self.local_diff_y < -self.local_diff_y_threshold: + self.yaw = 0.001 + if self.local_diff_y > self.local_diff_y_threshold: + self.yaw = -0.001 + + yaw_trans = self.bullet_client.getQuaternionFromEuler([0, 0, self.yaw]) + + if not self.is_far: + for i in range(len(self.toe_pos_local_ref)): + toe = self.toe_pos_local_ref[i] + toe, _ = self.bullet_client.multiplyTransforms([0, 0, 0], yaw_trans, + toe, [0, 0, 0, 1]) + self.toe_pos_local_ref[i] = toe + + new_toe_pos_world = [] + + # Moves the swing foot to the target location. + if self.move_swing_foot: + if self.state_time <= 1: + self.state_time += 4 * dt + if self.state_time >= 1: + self.move_swing_foot = False + self.state_time = 0 + self.toe_pos_local_ref[self.swing_foot_index] = self.new_pos_local + toe_pos_local_ref_copy = copy.deepcopy(self.toe_pos_local_ref) + old_pos = self.toe_pos_local_ref[self.swing_foot_index] + new_pos = [ + old_pos[0] * (1 - self.state_time) + self.new_pos_local[0] * + (self.state_time), old_pos[1] * (1 - self.state_time) + + self.new_pos_local[1] * (self.state_time), + old_pos[2] * (1 - self.state_time) + self.new_pos_local[2] * + (self.state_time) + self.amp * math.sin(self.state_time * math.pi) + ] + toe_pos_local_ref_copy[self.swing_foot_index] = new_pos + for toe_pos_local in toe_pos_local_ref_copy: + new_toe_pos_world.append(self.bullet_client.multiplyTransforms( + base_com_pos, base_com_orn, toe_pos_local, [0, 0, 0, 1])[0]) + + step_output = StepOutput(new_toe_pos_world) + return step_output diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/gait_generator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/gait_generator.py new file mode 100644 index 000000000..61bd849ac --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/gait_generator.py @@ -0,0 +1,32 @@ +"""Gait pattern planning module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import enum + + +class LegState(enum.Enum): + """The state of a leg during locomotion.""" + SWING = 0 + STANCE = 1 + # A swing leg that collides with the ground. + EARLY_CONTACT = 2 + # A stance leg that loses contact. + LOSE_CONTACT = 3 + + +class GaitGenerator(object): # pytype: disable=ignored-metaclass + """Generates the leg swing/stance pattern for the robot.""" + + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def reset(self, current_time): + pass + + @abc.abstractmethod + def update(self, current_time): + pass diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/imu_based_com_velocity_estimator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/imu_based_com_velocity_estimator.py new file mode 100644 index 000000000..4caea0906 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/imu_based_com_velocity_estimator.py @@ -0,0 +1,216 @@ +"""State estimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import Any, Sequence + +from filterpy import kalman +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import state_estimator +from pybullet_envs.minitaur.agents.baseline_controller import time_based_moving_window_filter +from pybullet_envs.minitaur.envs_v2.sensors import accelerometer_sensor +from pybullet_envs.minitaur.envs_v2.sensors import imu_sensor + +_DEFAULT_VELOCITY_FILTER_WINDOW = 0.2 +_DEFAULT_GYRO_FILTER_WINDOW = 0.1 +_DEFAULT_VELOCITY_CLIPPING = 0.8 +_STATE_DIMENSION = 3 +_GRAVITY = (0.0, 0.0, -9.8) + + +@gin.configurable +class IMUBasedCOMVelocityEstimator(state_estimator.StateEstimatorBase): + """Estimate the CoM velocity using IMU sensors and velocities of stance feet. + + + Estimates the com velocity of the robot using IMU data and stance feet + velocities fused by a Kalman Filter. Kalman Filter assumes the true state x + follows a linear dynamics: x'=Fx+Bu+w, where x' and x denots the + current and previous state of the robot, F is the state transition matrix, + B is the control-input matrix, and w is the noise in the dynamics, assuming to + follow a zero-mean multivariate normal distribution with covariance Q. It also + assumes that we can obtain a noisy observation of the state with the model + z=Hx+v, where z is the observed state, H is the observation matrix, and v is + a noise model, assuming to follow a zero-mean multivariate normal distribution + with covariance R. In our case, x is the CoM velocity of the robot, + F=B=H=eye(3), u=dt * CoM acceleration, which is obtained from accelerometer, + and the noisy observation z is obtained from the negated average velocities at + the end-effectors in contact with the ground. + + """ + + def __init__( + self, + robot: Any, + use_sensor_interface: bool = True, + accelerometer_variance=0.1, + observation_variance=0.1, + initial_variance=0.1, + velocity_filter_window: float = _DEFAULT_VELOCITY_FILTER_WINDOW, + gyroscope_filter_window: float = _DEFAULT_GYRO_FILTER_WINDOW, + contact_detection_threshold: float = 0.0, + velocity_clipping: float = _DEFAULT_VELOCITY_CLIPPING, + ): + """Initializes the class. + + Args: + robot: A quadruped robot. + use_sensor_interface: Whether to use the sensor interface to obtain the + IMU readings or directly get them from the robot class. Former should + be used in simulation to enable added latency and noise while latter + should be used on real robot for better performance. + accelerometer_variance: The estimated variance in the accelerometer + readings, used in the Kalman Filter. + observation_variance: The estimated variance in the observed CoM velocity + from the stance feet velocities, used in the Kalman Filter. + initial_variance: The variance of the initial distribution for the + estimated CoM variance. + velocity_filter_window: The filtering window (in time) used to smooth the + estimated CoM velocity. + gyroscope_filter_window: The filtering window (in time) used to smooth the + input gyroscope readings. + contact_detection_threshold: Threshold on the contact sensor readings to + determine whether the foot is in contact with the ground. + velocity_clipping: Clipping value for the estimated velocity to prevent + unrealistically large velocity estimations. + """ + self._robot = robot + self._contact_detection_threshold = contact_detection_threshold + self._velocity_clipping = velocity_clipping + self._use_sensor_interface = use_sensor_interface + + # Use the accelerometer and gyroscope sensor from the robot + if self._use_sensor_interface: + for sensor in self._robot.sensors: + if isinstance(sensor, accelerometer_sensor.AccelerometerSensor): + self._accelerometer = sensor + if isinstance(sensor, imu_sensor.IMUSensor): + self._gyroscope = sensor + assert hasattr(self, "_accelerometer") and self._accelerometer is not None + assert hasattr(self, "_gyroscope") and self._gyroscope is not None + + # x is the underlying CoM velocity we want to estimate, z is the observed + # CoM velocity from the stance feet velocities, and u is the accelerometer + # readings. + self._filter = kalman.KalmanFilter( + dim_x=_STATE_DIMENSION, + dim_z=_STATE_DIMENSION, + dim_u=_STATE_DIMENSION) + # Initialize the state distribution to be a zero-mean multi-variate normal + # distribution with initial variance. + self._filter.x = np.zeros(_STATE_DIMENSION) + self._initial_variance = initial_variance + self._filter.P = np.eye(_STATE_DIMENSION) * self._initial_variance + # Covariance matrix for the control variable. + self._filter.Q = np.eye(_STATE_DIMENSION) * accelerometer_variance + # Covariance matrix for the observed states. + self._filter.R = np.eye(_STATE_DIMENSION) * observation_variance + + # observation function (z=H*x+N(0,R)) + self._filter.H = np.eye(_STATE_DIMENSION) + # state transition matrix (x'=F*x+B*u+N(0,Q)) + self._filter.F = np.eye(_STATE_DIMENSION) + # Control transition matrix + self._filter.B = np.eye(_STATE_DIMENSION) + + self._velocity_filter = time_based_moving_window_filter.TimeBasedMovingWindowFilter( + velocity_filter_window) + self._gyroscope_filter = time_based_moving_window_filter.TimeBasedMovingWindowFilter( + gyroscope_filter_window) + + self.reset(0) + + @property + def com_velocity_body_yaw_aligned_frame(self) -> Sequence[float]: + """The base velocity projected in the body yaw aligned inertial frame. + + The body yaw aligned frame is a intertia frame where the z axis coincides + with the yaw of the robot base and the x and y axis coincides with the world + frame. It has a zero relative velocity/angular velocity + to the world frame. + + Returns: + The com velocity in body yaw aligned frame. + """ + clipped_velocity = np.clip(self._com_velocity_body_yaw_aligned_frame, + -self._velocity_clipping, + self._velocity_clipping) + + return clipped_velocity + + def reset(self, current_time): + del current_time + self._filter.x = np.zeros(_STATE_DIMENSION) + self._filter.P = np.eye(_STATE_DIMENSION) * self._initial_variance + + self._com_velocity_body_yaw_aligned_frame = np.zeros(_STATE_DIMENSION) + + self._velocity_filter.reset() + self._gyroscope_filter.reset() + + # Use None instead of 0 in case of a big gap between reset and first step. + self._last_timestamp = None + + def update(self, current_time): + del current_time + current_timestamp = self._robot.timestamp + + # First time step + if self._last_timestamp is None: + delta_time_s = 0.0 + else: + delta_time_s = current_timestamp - self._last_timestamp + self._last_timestamp = current_timestamp + + if self._use_sensor_interface: + sensor_acc = np.array(self._accelerometer.get_observation()) + gyroscope_reading = self._gyroscope.get_observation() + else: + sensor_acc = np.array(self._robot.base_acceleration_accelerometer) + gyroscope_reading = self._robot.base_roll_pitch_yaw + + filtered_gyroscope_reading = self._gyroscope_filter.calculate_average( + gyroscope_reading, current_timestamp) + # The yaw angle is not used here because reliably estimating the yaw angle + # of the robot is in general difficult. This leads to a body yaw aligned + # inertia frame for the estimated velocity. + yaw_aligned_base_orientation = self._robot.pybullet_client.getQuaternionFromEuler( + (filtered_gyroscope_reading[0], filtered_gyroscope_reading[1], 0.0)) + + rot_mat = self._robot.pybullet_client.getMatrixFromQuaternion( + yaw_aligned_base_orientation) + rot_mat = np.array(rot_mat).reshape((_STATE_DIMENSION, _STATE_DIMENSION)) + calibrated_acc = rot_mat.dot(sensor_acc) + np.array(_GRAVITY) + self._filter.predict(u=calibrated_acc * delta_time_s) + observed_velocities = [] + + foot_contact = [ + np.linalg.norm(contact_force) > self._contact_detection_threshold + for contact_force in self._robot.feet_contact_forces() + ] + for leg_id in range(4): + if foot_contact[leg_id]: + jacobian = self._robot.compute_jacobian_for_one_leg(leg_id) + # Only pick the jacobian related to joint motors + # TODO(magicmelon): standardize the process of picking out relevant dofs + com_dof = self._robot.urdf_loader.com_dof + jacobian = jacobian[:, + com_dof + leg_id * 3:com_dof + (leg_id + 1) * 3] + joint_velocities = self._robot.motor_velocities[leg_id * + 3:(leg_id + 1) * 3] + leg_velocity_in_base_frame = jacobian.dot(joint_velocities) + base_velocity_in_base_frame = -leg_velocity_in_base_frame + observed_velocities.append(rot_mat.dot(base_velocity_in_base_frame)) + + if observed_velocities: + observed_velocities = np.mean(observed_velocities, axis=0) + self._filter.update(observed_velocities) + + velocity = self._filter.x.copy() + + self._com_velocity_body_yaw_aligned_frame = self._velocity_filter.calculate_average( + velocity, current_timestamp) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/leg_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/leg_controller.py new file mode 100644 index 000000000..731f7ae9f --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/leg_controller.py @@ -0,0 +1,29 @@ +"""The leg controller class interface.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +from typing import Any + + +class LegController(object): # pytype: disable=ignored-metaclass + """Generates the leg control signal.""" + + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def reset(self, current_time: float): + """Resets the controller's internal state.""" + pass + + @abc.abstractmethod + def update(self, current_time: float): + """Updates the controller's internal state.""" + pass + + @abc.abstractmethod + def get_action(self) -> Any: + """Gets the control signal e.g. torques/positions for the leg.""" + pass diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller.py new file mode 100644 index 000000000..d9bca69f5 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller.py @@ -0,0 +1,96 @@ +"""A model based controller framework.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time +from typing import Any, Callable + +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator as gait_generator_lib +from pybullet_envs.minitaur.agents.baseline_controller import leg_controller as leg_controller_lib +from pybullet_envs.minitaur.agents.baseline_controller import state_estimator as state_estimator_lib + + +class LocomotionController(object): + """Generates the quadruped locomotion. + + The actual effect of this controller depends on the composition of each + individual subcomponent. + + """ + + def __init__( + self, + robot: Any, + gait_generator: gait_generator_lib.GaitGenerator, + state_estimator: state_estimator_lib.StateEstimatorBase, + swing_leg_controller: leg_controller_lib.LegController, + stance_leg_controller: leg_controller_lib.LegController, + clock: Callable[[], float] = None, + ): + """Initializes the class. + + Args: + robot: A robot instance. + gait_generator: Generates the leg swing/stance pattern. + state_estimator: Estimates the state of the robot (e.g. center of mass + position or velocity that may not be observable from sensors). + swing_leg_controller: Generates motor actions for swing legs. + stance_leg_controller: Generates motor actions for stance legs. + clock: A real or fake clock source. + """ + self._robot = robot + self._clock = clock + if clock is None: + self._clock = time.time + self._reset_time = self._clock() + self._time_since_reset = 0 + self._gait_generator = gait_generator + self._state_estimator = state_estimator + self._swing_leg_controller = swing_leg_controller + self._stance_leg_controller = stance_leg_controller + + @property + def swing_leg_controller(self): + return self._swing_leg_controller + + @property + def stance_leg_controller(self): + return self._stance_leg_controller + + @property + def gait_generator(self): + return self._gait_generator + + @property + def state_estimator(self): + return self._state_estimator + + def reset(self): + self._reset_time = self._clock() + self._time_since_reset = 0 + self._gait_generator.reset(self._time_since_reset) + self._state_estimator.reset(self._time_since_reset) + self._swing_leg_controller.reset(self._time_since_reset) + self._stance_leg_controller.reset(self._time_since_reset) + + def update(self): + self._time_since_reset = self._clock() - self._reset_time + self._gait_generator.update(self._time_since_reset) + self._state_estimator.update(self._time_since_reset) + self._swing_leg_controller.update(self._time_since_reset) + self._stance_leg_controller.update(self._time_since_reset) + + def get_action(self): + """Returns the control ouputs (e.g. positions/torques) for all motors.""" + swing_action = self._swing_leg_controller.get_action() + stance_action = self._stance_leg_controller.get_action() + action = [] + for joint_id in range(self._robot.num_motors): + if joint_id in swing_action: + action.extend(swing_action[joint_id]) + else: + assert joint_id in stance_action + action.extend(stance_action[joint_id]) + return action diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_example.py new file mode 100644 index 000000000..fd867950d --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_example.py @@ -0,0 +1,190 @@ +r"""Laikago walking example using the locomotion controller framework. + +""" + +import gc +import pickle + +from absl import app +from absl import flags +import numpy as np +import scipy.interpolate + + +from pybullet_envs.minitaur.agents.baseline_controller import locomotion_controller_setup +from pybullet_envs.minitaur.envs_v2 import env_loader +from pybullet_envs.minitaur.robots import robot_config + +FLAGS = flags.FLAGS +flags.DEFINE_boolean("run_on_robot", False, + "whether to run in sim or on real hardware") +flags.DEFINE_boolean( + "use_ground_truth_velocity", False, + "whether to use a ground truth velocity estimator (available in sim)") +flags.DEFINE_enum("gait", "fast_trot", + ["fast_trot", "slow_trot", "walk", "stand"], + "The gait pattern to use") +flags.DEFINE_boolean( + "use_keyboard_control", False, + "whether to use a keyboard to control or demo speed profile.") +flags.DEFINE_string("log_path", None, "Path to save robot logs") +flags.DEFINE_boolean("add_random_push", False, + "whether to add random push to the robot in simulation") + +_MAX_TIME_SECONDS = 100 + + +def _load_config(render=True, run_on_robot=False): + """Builds the environment for the quadruped robot. + + Args: + render: Enable/disable rendering. + run_on_robot: Whether deploy to robot or run in sim. + """ + if run_on_robot: + locomotion_controller_setup.load_real_config() + else: + locomotion_controller_setup.load_sim_config(render) + if FLAGS.add_random_push: + locomotion_controller_setup.add_random_push_config() + + +def _generate_example_linear_angular_speed(t): + """Creates an example speed profile based on time for demo purpose.""" + vx = 0.1 + vy = 0.1 + wz = 0.3 + time_points = (0, 4, 7, 11, 13, 15, 17, 19, 100) + speed_points = ((0, 0, 0, 0), (vx, 0, 0, 0), (-vx, 0, 0, 0), (0, -vy, 0, 0), + (0, vy, 0, 0), (0, 0, 0, wz), (0, 0, 0, -wz), (0, 0, 0, 0), + (0, 0, 0, 0)) + + speed = scipy.interpolate.interp1d( + time_points, + speed_points, + kind="previous", + fill_value="extrapolate", + axis=0)( + t) + + return speed[0:3], speed[3] + + +def _update_speed_from_kb(kb, lin_speed, ang_speed): + """Updates the controller behavior parameters.""" + if kb.is_keyboard_hit(): + c = kb.get_input_character() + if c == "w": + lin_speed += np.array((0.05, 0, 0)) + if c == "s": + lin_speed += np.array((-0.05, 0, 0)) + if c == "q": + ang_speed += 0.1 + if c == "e": + ang_speed += -0.1 + if c == "a": + lin_speed += np.array((0, 0.05, 0)) + if c == "d": + lin_speed += np.array((0, -0.05, 0)) + if c == "r": + lin_speed = np.array([0.0, 0.0, 0.0]) + ang_speed = 0.0 + + lin_speed[0] = np.clip(lin_speed[0], -0.2, 0.4) + lin_speed[1] = np.clip(lin_speed[1], -0.2, 0.2) + ang_speed = np.clip(ang_speed, -0.3, 0.3) + print("desired speed: ", lin_speed, ang_speed) + + return lin_speed, ang_speed + + +def _update_controller_params(controller, lin_speed, ang_speed): + controller.swing_leg_controller.desired_speed = lin_speed + controller.swing_leg_controller.desired_twisting_speed = ang_speed + controller.stance_leg_controller.desired_speed = lin_speed + controller.stance_leg_controller.desired_twisting_speed = ang_speed + + +def _run_example(max_time=_MAX_TIME_SECONDS, + run_on_robot=False, + use_keyboard=False): + """Runs the locomotion controller example.""" + if use_keyboard: + kb = keyboard_utils.KeyboardInput() + + env = env_loader.load() + env.reset() + + # To mitigate jittering from the python + gc.collect() + + # Wait for the robot to be placed properly. + if run_on_robot: + input("Press Enter to continue when robot is ready.") + + lin_speed = np.array([0.0, 0.0, 0.0]) + ang_speed = 0.0 + + controller = locomotion_controller_setup.setup_controller( + env.robot, FLAGS.gait, run_on_robot, FLAGS.use_ground_truth_velocity) + controller.reset() + + loop_start_time = env.get_time_since_reset() + loop_elapsed_time = 0 + robot_log = { + "timestamps": [], + "motor_angles": [], + "motor_velocities": [], + "base_velocities": [], + "foot_positions": [], + "base_rollpitchyaw": [], + "base_angular_velocities": [], + "actions": [] + } + try: + while loop_elapsed_time < max_time: + #if use_keyboard: + # lin_speed, ang_speed = _update_speed_from_kb(kb, lin_speed, ang_speed) + #else: + lin_speed, ang_speed = _generate_example_linear_angular_speed( + loop_elapsed_time) + + # Needed before every call to get_action(). + _update_controller_params(controller, lin_speed, ang_speed) + controller.update() + hybrid_action = controller.get_action() + + # Log the robot data. + robot_log["timestamps"].append(env.robot.GetTimeSinceReset()) + robot_log["motor_angles"].append(env.robot.motor_angles) + robot_log["motor_velocities"].append(env.robot.motor_velocities) + robot_log["base_velocities"].append( + controller.state_estimator.com_velocity_body_yaw_aligned_frame) + robot_log["foot_positions"].append(env.robot.foot_positions()) + robot_log["base_rollpitchyaw"].append(env.robot.base_roll_pitch_yaw) + robot_log["base_angular_velocities"].append( + env.robot.base_roll_pitch_yaw_rate) + robot_log["actions"].append(hybrid_action) + + env.step(hybrid_action) + loop_elapsed_time = env.get_time_since_reset() - loop_start_time + + finally: + if FLAGS.run_on_robot: + # Apply zero torques to the robot. + env.robot.apply_action( + [0] * env.robot.num_motors, + motor_control_mode=robot_config.MotorControlMode.TORQUE) + if FLAGS.log_path: + pickle.dump(robot_log, gfile.Open(FLAGS.log_path + "/robot.log", "wb")) + + +def main(argv): + del argv + _load_config(render=True, run_on_robot=FLAGS.run_on_robot) + _run_example( + run_on_robot=FLAGS.run_on_robot, use_keyboard=FLAGS.use_keyboard_control) + + +if __name__ == "__main__": + app.run(main) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_in_scenario_set_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_in_scenario_set_example.py new file mode 100644 index 000000000..71fefa7b5 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_in_scenario_set_example.py @@ -0,0 +1,180 @@ +r"""ScenarioSet example for Laikago MPC controller. + +blaze run -c opt \ +//robotics/reinforcement_learning/minitaur/agents/baseline_controller\ +:locomotion_controller_in_scenario_set_example -- --gait=slow_trot \ +--add_random_push=True +""" + +from absl import app +from absl import flags +import gin +import numpy as np +import scipy.interpolate + +from pybullet_envs.minitaur.agents.baseline_controller import locomotion_controller_setup +from pybullet_envs.minitaur.envs_v2 import env_loader + +FLAGS = flags.FLAGS + +SCENARIO_SET_CONFIG = """ +import pybullet_envs.minitaur.envs_v2.scenarios.locomotion_simple_scenario_set + +include "google3/robotics/reinforcement_learning/minitaur/envs_v2/scenarios/default_scenario_set.gin" + +default_scenario_set/singleton.constructor = @locomotion_simple_scenario_set.LocomotionSimpleScenarioSet + + +locomotion_simple_scenario_set.LocomotionSimpleScenarioSet.selector = "flat_ground" +locomotion_gym_env.LocomotionGymEnv.task = @scenario_set.task() +locomotion_gym_env.LocomotionGymEnv.scene = @scenario_set.scene() +locomotion_gym_env.LocomotionGymEnv.env_randomizers = [ + @scenario_set.env_randomizer() +] +""" + +_MAX_TIME_SECONDS = 30 + +flags.DEFINE_enum("gait", "fast_trot", + ["fast_trot", "slow_trot", "walk", "stand"], + "The gait pattern to use") + +flags.DEFINE_boolean("add_random_push", False, + "whether to add random push to the robot in simulation") + + +def _start_stop_profile(max_speed=0.5, axis=0, duration=3): + speed_profile = np.zeros((3, 4)) + + speed_profile[1, axis] = max_speed + + return (0, 0.5, duration + 0.5), speed_profile.tolist() + + +def _random_speed_profile(max_speed=1, axis=0, time_interval=1.0): + num_pts = 11 + time_points = np.arange(num_pts) * time_interval + + speed_profile = np.zeros((num_pts, 4)) + speed_profile[:, axis] = np.random.uniform(0, max_speed, num_pts) + speed_profile[-1, :] = 0 + return time_points.tolist(), speed_profile.tolist() + + +def _body_height_profile(z_range=(0.3, 0.55)): + del z_range + # TODO(tingnan): Implement this. + + +def _generate_linear_angular_speed(t, time_points, speed_points): + """Creates an example speed profile based on time for demo purpose.""" + + speed = scipy.interpolate.interp1d( + time_points, + speed_points, + kind="previous", + fill_value="extrapolate", + axis=0)( + t) + + return speed[0:3], speed[3] + + +def _update_controller_params(controller, lin_speed, ang_speed): + controller.swing_leg_controller.desired_speed = lin_speed + controller.swing_leg_controller.desired_twisting_speed = ang_speed + controller.stance_leg_controller.desired_speed = lin_speed + controller.stance_leg_controller.desired_twisting_speed = ang_speed + + +def _gen_stability_test_start_stop(): + """Generates the speed profile for start/stop tests.""" + axis_to_name = { + 0: "velocity x", + 1: "velocity y", + 3: "angular velocity z", + } + + axis_to_max_speed = { + 0: 1.0, + 1: 0.5, + 3: 2.5, + } + + gait_multiplier = { + "slow_trot": 0.7, + "walk": 0.3, + "fast_trot": 1.0, + } + + for axis in (0, 1, 3): + yield axis_to_name[axis], _start_stop_profile( + axis_to_max_speed[axis] * gait_multiplier[FLAGS.gait], axis) + + +def _gen_stability_test_random(): + """Generates the speed profile for random walking tests.""" + axis_to_name = { + 0: "velocity x", + 1: "velocity y", + 3: "angular velocity z", + } + + axis_to_max_speed = { + 0: 1.0, + 1: 0.5, + 3: 2.5, + } + + gait_multiplier = { + "slow_trot": 0.7, + "walk": 0.3, + "fast_trot": 1.0, + } + + for axis in (0, 1, 3): + yield axis_to_name[axis], _random_speed_profile( + axis_to_max_speed[axis] * gait_multiplier[FLAGS.gait], axis) + + +def _test_stability(max_time=5, render=False, test_generator=None): + """Tests the stability of the controller using speed profiles.""" + locomotion_controller_setup.load_sim_config(render=render) + gin.parse_config(SCENARIO_SET_CONFIG) + if FLAGS.add_random_push: + locomotion_controller_setup.add_random_push_config() + + env = env_loader.load() + controller = locomotion_controller_setup.setup_controller( + env.robot, gait=FLAGS.gait) + + for name, speed_profile in test_generator(): + env.reset() + controller.reset() + current_time = 0 + while current_time < max_time: + current_time = env.get_time_since_reset() + lin_speed, ang_speed = _generate_linear_angular_speed( + current_time, speed_profile[0], speed_profile[1]) + _update_controller_params(controller, lin_speed, ang_speed) + + # Needed before every call to get_action(). + controller.update() + hybrid_action = controller.get_action() + + _, _, done, _ = env.step(hybrid_action) + if done: + break + print(f"Scene name: flat ground. Random push: {FLAGS.add_random_push}. " + f"Survival time for {name} = {speed_profile[1]} is {current_time}") + + +def main(argv): + del argv + _test_stability(render=True, test_generator=_gen_stability_test_start_stop) + _test_stability( + max_time=15, render=True, test_generator=_gen_stability_test_random) + + +if __name__ == "__main__": + app.run(main) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_setup.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_setup.py new file mode 100644 index 000000000..afaed6cb7 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/locomotion_controller_setup.py @@ -0,0 +1,175 @@ +"""The common setups for MPC based locoomtion controller environments.""" +import time +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import com_velocity_estimator +from pybullet_envs.minitaur.agents.baseline_controller import imu_based_com_velocity_estimator +from pybullet_envs.minitaur.agents.baseline_controller import locomotion_controller +from pybullet_envs.minitaur.agents.baseline_controller import openloop_gait_generator +from pybullet_envs.minitaur.agents.baseline_controller import raibert_swing_leg_controller +from pybullet_envs.minitaur.agents.baseline_controller import torque_stance_leg_controller +#from pybullet_envs.minitaur.envs.env_randomizers import minitaur_push_randomizer +from pybullet_envs.minitaur.envs.env_randomizers import minitaur_push_randomizer +from pybullet_envs.minitaur.robots import robot_config +import pybullet_data as pd + +CONFIG_FILE = (pd.getDataPath()+"/configs_v2/base/laikago_reactive.gin") + +_MOTOR_KD = [1.0, 2.0, 2.0] * 4 +_BODY_HEIGHT = 0.45 +_MAX_TIME_SECONDS = 1000000000 +_MOTOR_KD = [1.0, 2.0, 2.0] * 4 +# TODO(tingnan): This is for tunining the moments of inertia of the model. +# Once we identified the correct value we can remove this. +_SCALE = 4 +_INERTIA = (0.07335 * _SCALE, 0, 0, 0, 0.25068 * _SCALE, 0, 0, 0, + 0.25447 * _SCALE) + + +def load_sim_config(render=True): + """Builds the environment for the quadruped robot. + + Args: + render: Enable/disable rendering. + """ + gin.clear_config(clear_constants=False) + config_file = CONFIG_FILE + gin.parse_config_file(config_file) + + # Sim bindings + # Overwrite a few parameters. + + action_repeat = 4 + gin.bind_parameter("SimulationParameters.num_action_repeat", action_repeat) + gin.bind_parameter("laikago_v2.Laikago.action_repeat", action_repeat) + + # Control latency is NOT modeled properly for inverse kinematics and + # jacobians, as we are directly calling the pybullet API. We will try to fix + # this by loading a separate pybullet instance, set the pose and joint + # angles which has latency in them, and then run the jacobian/IK. + gin.bind_parameter("laikago_v2.Laikago.motor_control_mode", + robot_config.MotorControlMode.HYBRID) + # Bump up a bit the adduction/abduction motor d gain for a better tracking. + gin.bind_parameter("hybrid_motor_model.HybridMotorModel.kd", _MOTOR_KD) + gin.bind_parameter("SimulationParameters.enable_rendering", render) + gin.bind_parameter("env_loader.load.wrapper_classes", []) + + + +def add_random_push_config(): + """Adds a random push randomizers to the config.""" + try: + current_env_randomizers = gin.query_parameter( + "locomotion_gym_env.LocomotionGymEnv.env_randomizers") + + current_env_randomizers.append( + minitaur_push_randomizer.MinitaurPushRandomizer( + horizontal_force_bound=(500, 900), + vertical_force_bound=(50, 100), + visualize_perturbation_force=True)) + gin.bind_parameter("locomotion_gym_env.LocomotionGymEnv.env_randomizers", + current_env_randomizers) + except ValueError: + # No randoimzers bind so far + gin.bind_parameter("locomotion_gym_env.LocomotionGymEnv.env_randomizers", [ + minitaur_push_randomizer.MinitaurPushRandomizer( + horizontal_force_bound=(500, 900), + vertical_force_bound=(50, 100), + visualize_perturbation_force=True) + ]) + + +def select_gait(gait_type="fast_trot"): + """Selects a gait pattern. + + Args: + gait_type: which gait to use. + + Returns: + A tuple of (stance_duration, duty_factor, initial_phase) + """ + # Each gait is composed of stance_duration, duty_factor, and + # init_phase_full_cycle. + if gait_type == "fast_trot": + return [0.25] * 4, [0.6] * 4, [0, 0.5, 0.5, 0] + elif gait_type == "slow_trot": + return [0.4] * 4, [0.65] * 4, [0, 0.5, 0.5, 0] + elif gait_type == "walk": + return [0.75] * 4, [0.8] * 4, [0.25, 0.75, 0.5, 0] + else: + # Means four leg stand for as long as possible. + return [_MAX_TIME_SECONDS] * 4, [0.99] * 4, [0, 0, 0, 0] + + +def setup_controller(robot, + gait="fast_trot", + run_on_robot=False, + use_ground_truth_velocity=False): + """Demonstrates how to create a locomotion controller. + + Args: + robot: A robot instance. + gait: The type of gait to use. + run_on_robot: Whether this controller is running on the real robot or not. + use_ground_truth_velocity: Whether to use ground truth velocity or velocity + estimator. + + Returns: + A locomotion controller. + """ + desired_speed = (0, 0) + desired_twisting_speed = 0 + + feet_positions = np.array(robot.foot_positions()) + feet_positions[:, 2] = 0 + + # Sim and real robots have different mass and contact detection parameters. + body_weight, contact_force_threshold = (200, 20) if run_on_robot else (215, 0) + + stance_duration, duty_factor, init_phase = select_gait(gait) + gait_generator = openloop_gait_generator.OpenloopGaitGenerator( + robot, + stance_duration=stance_duration, + duty_factor=duty_factor, + initial_leg_phase=init_phase, + contact_detection_force_threshold=contact_force_threshold, + ) + state_estimator = ( + imu_based_com_velocity_estimator.IMUBasedCOMVelocityEstimator( + robot, + contact_detection_threshold=contact_force_threshold, + )) + + # Use this in sim to test ground truth velocity estimation. + if use_ground_truth_velocity: + state_estimator = com_velocity_estimator.COMVelocityEstimator(robot) + + sw_controller = raibert_swing_leg_controller.RaibertSwingLegController( + robot, + gait_generator, + state_estimator, + desired_speed=desired_speed, + desired_twisting_speed=desired_twisting_speed, + desired_height=_BODY_HEIGHT, + local_hip_positions=feet_positions, + ) + st_controller = torque_stance_leg_controller.TorqueStanceLegController( + robot, + gait_generator, + state_estimator, + desired_speed=desired_speed, + desired_twisting_speed=desired_twisting_speed, + desired_body_height=_BODY_HEIGHT, + body_mass=body_weight / 9.8, + body_inertia=_INERTIA, + ) + + controller = locomotion_controller.LocomotionController( + robot=robot, + gait_generator=gait_generator, + state_estimator=state_estimator, + swing_leg_controller=sw_controller, + stance_leg_controller=st_controller, + clock=robot.GetTimeSinceReset) + return controller diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller.py new file mode 100644 index 000000000..82ddebcab --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller.py @@ -0,0 +1,448 @@ +"""A Raibert style controller for Minitaur.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import attr +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import minitaur_raibert_controller_utils +from pybullet_envs.minitaur.envs.utilities import minitaur_pose_utils + +LEFT_FRONT_LEG_ID = 0 +LEFT_HIND_LEG_ID = 1 +RIGHT_FRONT_LEG_ID = 2 +RIGHT_HIND_LEG_ID = 3 + +DIAGONAL_LEG_PAIR_1 = (LEFT_FRONT_LEG_ID, RIGHT_HIND_LEG_ID) +DIAGONAL_LEG_PAIR_2 = (LEFT_HIND_LEG_ID, RIGHT_FRONT_LEG_ID) + +LEFT_LEG_IDS = (LEFT_FRONT_LEG_ID, LEFT_HIND_LEG_ID) +RIGHT_LEG_IDS = (RIGHT_FRONT_LEG_ID, RIGHT_HIND_LEG_ID) + +# The max horizontal foot offset (in meters) for turning. +_FOOT_HORIZONTAL_OFFSET_FOR_TURNING = 0.1 + +_STANCE_TG_PHASE_COMPRESSION = 1.5 +_STANCE_TG_DELTA_EXTENSION = 0.2 +_STANCE_HORIZONTAL_SCALING_FACTOR = 1.2 + +_DEFAULT_SWING_SPEED_GAIN = 0.015 + +_DEFAULT_SWING_FOOT_CLEARANCE = 0.005 + +_PITCH_SWING_FEEDBACK_SCALING_FACTOR = 1.2 + + +# A POD container to describe the controller's high level behavior. +@attr.s +class BehaviorParameters(object): + """Highlevel parameters for Raibert style controller.""" + stance_duration = attr.ib(type=float, default=0.25) + desired_forward_speed = attr.ib(type=float, default=0.) + desired_turning_speed = attr.ib(type=float, default=0.) + standing_height = attr.ib(type=float, default=0.2) + desired_incline_angle = attr.ib(type=float, default=0.) + + +def generate_default_swing_trajectory(phase, init_pose, end_pose): + """A swing trajectory generator. + + Args: + phase: Float. Between [0, 1]. + init_pose: A tuple. The leg pose (swing, extension) at phase == 0 the + beginning of swing. + end_pose: A tuple. The leg pose at phase == 1 the end of swing. + + Returns: + The desired leg pose for the current phase. + """ + # Try phase compression + normalized_phase = math.sqrt(min(phase * 1.5, 1)) + + # For swing, we use a linear interpolation: + swing = (end_pose[0] - init_pose[0]) * normalized_phase + init_pose[0] + + # For extension, we can fit a second order polynomial: + min_ext = (init_pose[1] + end_pose[1]) / 2 - 0.8 + min_ext = max(min_ext, 0.5) + + # The phase value at which the extension reaches the minimal value min_ext. + # phi is small, the swing leg will try to lift higher in the first half of + # swing. + phi = 0.1 + + # We convert the extension back into the cartesion space. In this way we can + # guarantee a lift-up trajectory. The ankle to hip distance is easier to + # compute than a full forward-kinematics. + min_ankle_dist = minitaur_raibert_controller_utils.extension_to_ankle_dist( + min_ext) + init_ankle_dist = minitaur_raibert_controller_utils.extension_to_ankle_dist( + init_pose[1]) + end_ankle_dist = minitaur_raibert_controller_utils.extension_to_ankle_dist( + end_pose[1]) + + # The polynomial is: a * phi^2 + b * phi + c + delta_1 = min_ankle_dist - init_ankle_dist + delta_2 = end_ankle_dist - init_ankle_dist + delta_p = phi * phi - phi + + a = (delta_1 - phi * delta_2) / delta_p + + b = (phi * phi * delta_2 - delta_1) / delta_p + + ankle_dist = ( + a * normalized_phase * normalized_phase + b * normalized_phase + + init_ankle_dist) + + l1 = minitaur_raibert_controller_utils.UPPER_LEG_LEN + l2 = minitaur_raibert_controller_utils.LOWER_SHORT_LEG_LEN + + ankle_dist = min(max(ankle_dist, l2 - l1 + 0.01), l2 + l1 - 0.01) + + extension = minitaur_raibert_controller_utils.ankle_dist_to_extension( + ankle_dist) + + return (swing, extension) + + +@gin.configurable +def generate_default_stance_trajectory(phase, + init_pose, + end_pose, + use_constant_extension=False): + """A stance strajectory generator. + + Args: + phase: Float. Between [0, 1]. + init_pose: A tuple. The leg pose (swing, extension) at phase == 0, i.e. the + beginning of stance. + end_pose: A tuple. The leg pose at phase == 1, i.e. the end of stance. + use_constant_extension: Boolean. Whether or not to fix the extension during + stance. + + Returns: + The desired leg pose for the current phase. + """ + normalized_phase = min(_STANCE_TG_PHASE_COMPRESSION * math.sqrt(phase), 1) + swing = (end_pose[0] - init_pose[0]) * normalized_phase + init_pose[0] + + # The extension evolves nonlinearly according to the parabola equation. + if use_constant_extension: + extension = end_pose[1] + else: + extension = end_pose[1] - 4 * _STANCE_TG_DELTA_EXTENSION * ( + normalized_phase**2 - normalized_phase) + return (swing, extension) + + +def get_stance_foot_offset_for_turning(leg_id, steering_signal): + """Modify the stance foot position to achieve turning. + + The strategy works for trotting gaits. + + Args: + leg_id: Integer. The leg index. + steering_signal: Float. The desired turning signal in [-1, 1]. Because we + don't have an accurate mapping from angular velocity to the foot offset, + the steering signal should be treated as a reference and only its relative + magnitude matters. + + Returns: + The stance foot's horizontal offset. + + """ + clipped_steering_signal = np.clip(steering_signal, -1, 1) + + if leg_id in LEFT_LEG_IDS: + return _FOOT_HORIZONTAL_OFFSET_FOR_TURNING * clipped_steering_signal + else: + return -(_FOOT_HORIZONTAL_OFFSET_FOR_TURNING * clipped_steering_signal) + + +def get_leg_swing_offset_for_pitching(body_pitch, desired_incline_angle): + """Get the leg swing zero point when the body is tilted. + + For example, when climbing up or down stairs/slopes, the robot body will tilt + up or down. By compensating the body pitch, the leg's trajectory will be + centered around the vertical direction (not perpendicular to the surface). + This helps the robot to generate thrust when going upwards, and braking when + going downwards. + + Args: + body_pitch: Float. Current body pitch angle. + desired_incline_angle: Float. The desired body pitch angle. + + Returns: + The stance and swing leg swing offset. + + """ + kp = 0.2 + return -((1 - kp) * body_pitch + kp * desired_incline_angle) + + +@gin.configurable +class RaibertSwingLegController(object): + """The swing leg controller.""" + + def __init__(self, + speed_gain=_DEFAULT_SWING_SPEED_GAIN, + foot_clearance=_DEFAULT_SWING_FOOT_CLEARANCE, + leg_trajectory_generator=generate_default_swing_trajectory): + """Initializes the controller. + + Args: + speed_gain: Float. The speed feedback gain to modulate the target foot + position. + foot_clearance: Float. The foot clearance (at the end of the swing phase) + in meters. + leg_trajectory_generator: A trajectory generator function. + """ + self._speed_gain = speed_gain + self._foot_clearance = foot_clearance + self._leg_trajectory_generator = leg_trajectory_generator + + def get_action(self, raibert_controller): + """Get the swing legs' desired pose.""" + current_speed = raibert_controller.estimate_base_velocity() + phase = raibert_controller.get_phase() + rpy = raibert_controller.robot.base_roll_pitch_yaw + + leg_pose_set = {} + for i in raibert_controller.swing_set: + # Target foot horizontal position is calculated according to Raibert's + # original formula in "Legged robots that balance". + target_foot_horizontal_position = ( + raibert_controller.behavior_parameters.stance_duration / 2 * + current_speed + self._speed_gain * + (current_speed - + raibert_controller.behavior_parameters.desired_forward_speed)) + + # 1) Convert the target foot position to leg pose space. + # Lift the foot a little bit. + target_foot_vertical_position = -( + raibert_controller.behavior_parameters.standing_height - + self._foot_clearance) + target_foot_position = (target_foot_horizontal_position, + target_foot_vertical_position) + target_leg_pose = minitaur_raibert_controller_utils.foot_position_to_leg_pose( + target_foot_position) + + # 2) Generates the curve from the swing start leg pose to the target leg + # pose and find the next leg pose on the curve based on current swing + # phase. + + desired_leg_pose = self._leg_trajectory_generator( + phase, raibert_controller.swing_start_leg_pose, target_leg_pose) + + swing_offset = get_leg_swing_offset_for_pitching( + rpy[1], raibert_controller.behavior_parameters.desired_incline_angle) + + leg_pose_set[i] = (desired_leg_pose[0] + swing_offset, + desired_leg_pose[1]) + + return leg_pose_set + + +@gin.configurable +class RaibertStanceLegController(object): + """The controller that modulates the behavior of the stance legs.""" + + def __init__(self, + speed_gain=0.1, + leg_trajectory_generator=generate_default_stance_trajectory): + """Initializes the controller. + + Args: + speed_gain: Float. The speed feedback gain to modulate the target stance + foot position. + leg_trajectory_generator: A trajectory generator function. Generates the + desired leg pose during the stance phase. + """ + self._speed_gain = speed_gain + self._leg_trajectory_generator = leg_trajectory_generator + + def get_action(self, raibert_controller): + """Get the desired leg pose for the stance legs.""" + + phase = raibert_controller.get_phase() + current_speed = raibert_controller.estimate_base_velocity() + rpy = raibert_controller.robot.base_roll_pitch_yaw + + leg_pose_set = {} + for i in raibert_controller.stance_set: + desired_forward_speed = ( + raibert_controller.behavior_parameters.desired_forward_speed) + + target_foot_horizontal_position = -_STANCE_HORIZONTAL_SCALING_FACTOR * ( + raibert_controller.behavior_parameters.stance_duration / 2 * + current_speed - self._speed_gain * + (current_speed - desired_forward_speed)) + + target_foot_horizontal_position += get_stance_foot_offset_for_turning( + i, raibert_controller.behavior_parameters.desired_turning_speed) + + target_foot_position = ( + target_foot_horizontal_position, + -raibert_controller.behavior_parameters.standing_height) + target_leg_pose = minitaur_raibert_controller_utils.foot_position_to_leg_pose( + target_foot_position) + + desired_leg_pose = ( + self._leg_trajectory_generator( + phase, raibert_controller.stance_start_leg_pose, target_leg_pose)) + + swing_offset = _PITCH_SWING_FEEDBACK_SCALING_FACTOR * get_leg_swing_offset_for_pitching( + rpy[1], raibert_controller.behavior_parameters.desired_incline_angle) + + leg_pose_set[i] = (desired_leg_pose[0] + swing_offset, + desired_leg_pose[1]) + + return leg_pose_set + + +@gin.configurable +class MinitaurRaibertController(object): + """A Raibert style controller for trotting gait.""" + + def __init__(self, + robot, + behavior_parameters=BehaviorParameters(), + swing_leg_controller=RaibertSwingLegController(), + stance_leg_controller=RaibertStanceLegController(), + pose_feedback_controller=None): + self._time = 0 + self._robot = robot + self.behavior_parameters = behavior_parameters + + self._last_recorded_speed = 0 + + self._swing_leg_controller = swing_leg_controller + self._stance_leg_controller = stance_leg_controller + self._pose_feeback_controller = pose_feedback_controller + + # The leg order is FL, RL, FR, RR -> [0, 1, 2, 3] + self._swing_set = DIAGONAL_LEG_PAIR_1 + self._stance_set = DIAGONAL_LEG_PAIR_2 + + # Compute the initial leg pose. + self._swing_start_leg_pose = self.get_swing_leg_pose() + self._stance_start_leg_pose = self.get_stance_leg_pose() + + @property + def robot(self): + return self._robot + + @property + def swing_set(self): + return self._swing_set + + @property + def stance_set(self): + return self._stance_set + + @property + def swing_start_leg_pose(self): + return self._swing_start_leg_pose + + @property + def stance_start_leg_pose(self): + return self._stance_start_leg_pose + + def _get_average_leg_pose(self, leg_indices): + """Get the average leg pose.""" + current_leg_pose = minitaur_pose_utils.motor_angles_to_leg_pose( + self._robot.motor_angles) + + # extract the swing leg pose from the current_leg_pose + leg_pose = [] + for index in leg_indices: + leg_pose.append([ + current_leg_pose[index], + current_leg_pose[index + minitaur_pose_utils.NUM_LEGS] + ]) + + leg_pose = np.array(leg_pose) + return np.mean(leg_pose, axis=0) + + def get_swing_leg_pose(self): + """Get the current swing legs' average pose.""" + return self._get_average_leg_pose(self._swing_set) + + def get_stance_leg_pose(self): + """Get the current stance legs' average pose.""" + return self._get_average_leg_pose(self._stance_set) + + def get_phase(self): + """Compute the current stance/swing phase.""" + return math.fmod(self._time, self.behavior_parameters.stance_duration + ) / self.behavior_parameters.stance_duration + + def _get_new_swing_stance_set(self): + """Switch the set of swing/stance legs based on timing.""" + swing_stance_phase = math.fmod(self._time, + 2 * self.behavior_parameters.stance_duration) + if swing_stance_phase < self.behavior_parameters.stance_duration: + return (DIAGONAL_LEG_PAIR_1, DIAGONAL_LEG_PAIR_2) + return (DIAGONAL_LEG_PAIR_2, DIAGONAL_LEG_PAIR_1) + + def update(self, t): + """Update the internal status of the controller. + + Args: + t: Float. The current time after reset in seconds. + """ + self._time = t + new_swing_set, new_stance_set = self._get_new_swing_stance_set() + + # If there is a stance/swing switch. + if new_swing_set[0] is not self._swing_set[0]: + self._swing_set = new_swing_set + self._stance_set = new_stance_set + + # Also records the starting pose. + self._swing_start_leg_pose = self.get_swing_leg_pose() + self._stance_start_leg_pose = self.get_stance_leg_pose() + + def estimate_base_velocity(self): + """Estimate the current CoM speed.""" + # It is best to use a sensor fusion approach. + stance_leg_pose = self.get_stance_leg_pose() + + delta_sw = stance_leg_pose[0] - self._stance_start_leg_pose[0] + + x, y = minitaur_raibert_controller_utils.leg_pose_to_foot_position( + stance_leg_pose) + toe_hip_len = math.sqrt(x**2 + y**2) + horizontal_dist = toe_hip_len * delta_sw + phase = self.get_phase() + speed = self._last_recorded_speed + if phase > 0.1: + speed = horizontal_dist / ( + self.behavior_parameters.stance_duration * phase) + self._last_recorded_speed = speed + return speed + + def get_swing_leg_action(self): + return self._swing_leg_controller.get_action(self) + + def get_stance_leg_action(self): + return self._stance_leg_controller.get_action(self) + + def get_action(self): + """Gets the desired motor angles.""" + leg_pose = [0] * minitaur_pose_utils.NUM_MOTORS + swing_leg_pose = self.get_swing_leg_action() + for i in self._swing_set: + leg_pose[i] = swing_leg_pose[i][0] + leg_pose[i + minitaur_pose_utils.NUM_LEGS] = swing_leg_pose[i][1] + + stance_leg_pose = self.get_stance_leg_action() + for i in self._stance_set: + leg_pose[i] = stance_leg_pose[i][0] + leg_pose[i + minitaur_pose_utils.NUM_LEGS] = stance_leg_pose[i][1] + + return minitaur_pose_utils.leg_pose_to_motor_angles(leg_pose) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller_utils.py new file mode 100644 index 000000000..cc992a197 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/minitaur_raibert_controller_utils.py @@ -0,0 +1,82 @@ +"""Utility functions for the Minitaur Raibert controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +UPPER_LEG_LEN = 0.112 +LOWER_SHORT_LEG_LEN = 0.199 +LOWER_LONG_LEG_LEN = 0.2315 + + +def leg_pose_to_foot_position(leg_pose): + """The forward kinematics.""" + l1 = UPPER_LEG_LEN + l2 = LOWER_SHORT_LEG_LEN + l3 = LOWER_LONG_LEG_LEN + + ext = leg_pose[1] + alpha = math.asin(l1 * math.sin(ext) / l2) + + sw = -leg_pose[0] + x = l3 * math.sin(alpha + sw) - l1 * math.sin(ext + sw) + y = l3 * math.cos(alpha + sw) - l1 * math.cos(ext + sw) + + return (x, -y) + + +def foot_position_to_leg_pose(foot_position): + """The inverse kinematics.""" + l1 = UPPER_LEG_LEN + l2 = LOWER_SHORT_LEG_LEN + l3 = LOWER_LONG_LEG_LEN + + x = foot_position[0] + y = foot_position[1] + + assert y < 0 + hip_toe_sqr = x**2 + y**2 + cos_beta = (l1 * l1 + l3 * l3 - hip_toe_sqr) / (2 * l1 * l3) + assert -1 <= cos_beta <= 1 + hip_ankle_sqr = l1 * l1 + l2 * l2 - 2 * l1 * l2 * cos_beta + hip_ankle = math.sqrt(hip_ankle_sqr) + cos_ext = -(l1 * l1 + hip_ankle_sqr - l2 * l2) / (2 * l1 * hip_ankle) + ext = math.acos(cos_ext) + + hip_toe = math.sqrt(hip_toe_sqr) + cos_theta = (hip_toe_sqr + hip_ankle_sqr - + (l3 - l2)**2) / (2 * hip_ankle * hip_toe) + + assert cos_theta > 0 + theta = math.acos(cos_theta) + sw = math.asin(x / hip_toe) - theta + return (-sw, ext) + + +def extension_to_ankle_dist(extension): + """Converts leg extension to ankle-hip distance in meters. + + The ankle is defined as the joint of the two lower links, which is different + from the toe which is the tip of the longer lower limb. + + Args: + extension: Float. the leg extension. + + Returns: + Float. The hip to ankle distance in meters. + + """ + l1 = UPPER_LEG_LEN + l2 = LOWER_SHORT_LEG_LEN + alpha = math.asin(l1 / l2 * math.sin(extension)) + return l2 * math.cos(alpha) - l1 * math.cos(extension) + + +def ankle_dist_to_extension(dist): + """Converts ankle-hip distance (meters) to extension.""" + l1 = UPPER_LEG_LEN + l2 = LOWER_SHORT_LEG_LEN + cos_extension = -(l1**2 + dist**2 - l2**2) / (2 * l1 * dist) + return math.acos(cos_extension) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/model_predictive_control.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/model_predictive_control.py new file mode 100644 index 000000000..739424360 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/model_predictive_control.py @@ -0,0 +1,149 @@ +# Lint as: python3 +"""Classic model predictive control methods.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +from typing import Sequence +import numpy as np + +_MAX_ABS_RPY = 0.3 +_MAX_ABS_ANGULAR_VELOCITY = math.pi + +# The center of mass torque is computed using a simple PD control: tau = -KP * +# delta_rotation - KD * delta_angular_velocity +_TORQUE_KP = 2000 +_TORQUE_KD = 150 + +# For center of mass force, we only need to track position in the z direction +# (i.e. maintain the body height), and speed in x-y plane. +_FORCE_KP = 500 +_FORCE_KD = 200 + + +def compute_contact_force_projection_matrix( + foot_positions_in_com_frame: Sequence[Sequence[float]], + stance_foot_ids: Sequence[int], +) -> np.ndarray: + r"""Computes the 6 x 3n matrix to map contact force to com dynamics. + + This is essentially the vectorized rhs of com dynamics equation: + ma = \sum f + I\omega_dot = \sum r \cross f + + where the summation if over all feet in contact with ground. + + Caveats: Current we have not taken the com rotation into account as we assume + the com rotation would be small. Ideally we should rotate the foot_positions + to a world frame centered at com. Also, since absolute yaw angles are not + accurate dute to drifting, we should use (roll, pitch, 0) to do the foot + position projection. This feature will be quite useful for MPC. + TODO(b/143378213): Fix this. + + Args: + foot_positions_in_com_frame: the local position of each foot. + stance_foot_ids: The stance foot to be used to assemble the matrix. + + Returns: + The contact force projection matrix. + + """ + jacobians = [] + for foot_id in stance_foot_ids: + jv = np.identity(3) + foot_position = foot_positions_in_com_frame[foot_id] + x, y, z = foot_position[:3] + jw = np.array(((0, -z, y), (z, 0, -x), (-y, x, 0))) + jacobians.append(np.vstack((jv, jw))) + + return np.hstack(jacobians) + + +def plan_foot_contact_force( + mass: float, + inertia: np.ndarray, + com_position: np.ndarray, + com_velocity: np.ndarray, + com_roll_pitch_yaw: np.ndarray, + com_angular_velocity: np.ndarray, + foot_positions_in_com_frame: Sequence[Sequence[float]], + foot_contact_state: Sequence[bool], + desired_com_position: np.ndarray, + desired_com_velocity: np.ndarray, + desired_com_roll_pitch_yaw: np.ndarray, + desired_com_angular_velocity: np.ndarray, +): + """Plan the foot contact forces using robot states. + + TODO(b/143382305): Wrap this interface in a MPC class so we can use other + planning algorithms. + + Args: + mass: The total mass of the robot. + inertia: The diagnal elements [Ixx, Iyy, Izz] of the robot. + com_position: Center of mass position in world frame. Usually we cannot + accurrately obtain this without motion capture. + com_velocity: Center of mass velocity in world frame. + com_roll_pitch_yaw: Center of mass rotation wrt world frame in euler angles. + com_angular_velocity: The angular velocity (roll_dot, pitch_dot, yaw_dot). + foot_positions_in_com_frame: The position of all feet/toe joints in the body + frame. + foot_contact_state: Indicates if a foot is in contact with the ground. + desired_com_position: We usually just care about the body height. + desired_com_velocity: In world frame. + desired_com_roll_pitch_yaw: We usually care about roll and pitch, since yaw + measurement can be unreliable. + desired_com_angular_velocity: Roll and pitch change rate are usually zero. + Yaw rate is the turning speed of the robot. + + Returns: + The desired stance foot contact forces. + """ + del inertia + del com_position + body_height = [] + stance_foot_ids = [] + for foot_id, foot_position in enumerate(foot_positions_in_com_frame): + if not foot_contact_state[foot_id]: + continue + stance_foot_ids.append(foot_id) + body_height.append(foot_position[2]) + + avg_bogy_height = abs(sum(body_height) / len(body_height)) + + rpy = com_roll_pitch_yaw + rpy[:2] = np.clip(rpy[:2], -_MAX_ABS_RPY, _MAX_ABS_RPY) + rpy_dot = com_angular_velocity + rpy_dot = np.clip(rpy_dot, -_MAX_ABS_ANGULAR_VELOCITY, + _MAX_ABS_ANGULAR_VELOCITY) + + com_torque = -avg_bogy_height * ( + _TORQUE_KP * (rpy - desired_com_roll_pitch_yaw) + _TORQUE_KD * rpy_dot) + + # We don't care about the absolute yaw angle in the low level controller. + # Instead, we stabialize the angular velocity in the z direction. + com_torque[2] = -avg_bogy_height * _TORQUE_KD * ( + rpy_dot[2] - desired_com_angular_velocity[2]) + + # Track a desired com velocity. + com_force = -_FORCE_KD * (com_velocity - desired_com_velocity) + + # In the z-direction we also want to track the body height. + com_force[2] += mass * 9.8 - _FORCE_KP * ( + avg_bogy_height - desired_com_position[2]) + + com_force_torque = np.concatenate((com_force, com_torque)).transpose() + + # Map the com force torque to foot contact forces. + foot_force_to_com = compute_contact_force_projection_matrix( + foot_positions_in_com_frame, stance_foot_ids) + all_contact_force = -np.matmul( + np.linalg.pinv(foot_force_to_com), com_force_torque).transpose() + contact_force = {} + + for i, foot_id in enumerate(stance_foot_ids): + contact_force[foot_id] = all_contact_force[3 * i:3 * i + 3] + + return contact_force diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/multi_state_estimator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/multi_state_estimator.py new file mode 100644 index 000000000..60f8cd86b --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/multi_state_estimator.py @@ -0,0 +1,48 @@ +"""A class for combining multiple state estimators.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import Any, Sequence + +import gin + +from pybullet_envs.minitaur.agents.baseline_controller import state_estimator + + +@gin.configurable +class MultiStateEstimator(state_estimator.StateEstimatorBase): + """Combine multiple state estimators. + + + This class can be used to combine multiple state estimators into one. For + example, one can use the COMVelocityEstimator to estimate the com velocity + and COMHeightEstimator to estimate the com height. + + """ + + def __init__( + self, + robot: Any, + state_estimators: Sequence[state_estimator.StateEstimatorBase], + ): + self._robot = robot + self._state_estimators = state_estimators + self.reset(0) + + def reset(self, current_time): + for single_state_estimator in self._state_estimators: + single_state_estimator.reset(current_time) + + def update(self, current_time): + for single_state_estimator in self._state_estimators: + single_state_estimator.update(current_time) + + def __getattr__(self, attr): + for single_state_estimator in self._state_estimators: + if hasattr(single_state_estimator, attr): + return getattr(single_state_estimator, attr) + raise ValueError( + "{} is not found in any of the state estimators".format(attr)) + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/openloop_gait_generator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/openloop_gait_generator.py new file mode 100644 index 000000000..c8696dc27 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/openloop_gait_generator.py @@ -0,0 +1,194 @@ +"""Gait pattern planning module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import logging +import math +from typing import Any, Sequence + +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator + +_DEFAULT_INITIAL_LEG_STATE = ( + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, + gait_generator.LegState.STANCE, +) + +_NOMINAL_STANCE_DURATION = (0.25, 0.25, 0.25, 0.25) +_NOMINAL_DUTY_FACTOR = (0.6, 0.6, 0.6, 0.6) +_TROTTING_LEG_PHASE = (0, 0.5, 0.5, 0) +_NOMINAL_CONTACT_DETECTION_PHASE = 0.4 + + +@gin.configurable +class OpenloopGaitGenerator(gait_generator.GaitGenerator): + """Generates openloop gaits for quadruped robots. + + A flexible open-loop gait generator. Each leg has its own cycle and duty + factor. And the state of each leg alternates between stance and swing. One can + easily formuate a set of common quadruped gaits like trotting, pacing, + pronking, bounding, etc by tweaking the input parameters. + """ + + def __init__( + self, + robot: Any, + stance_duration: Sequence[float] = _NOMINAL_STANCE_DURATION, + duty_factor: Sequence[float] = _NOMINAL_DUTY_FACTOR, + initial_leg_phase: Sequence[float] = _TROTTING_LEG_PHASE, + contact_detection_force_threshold: float = 0, + contact_detection_phase_threshold: + float = _NOMINAL_CONTACT_DETECTION_PHASE, + ): + """Initializes the class. + + Args: + robot: A quadruped robot that at least implements the GetFootContacts API + and num_legs property. + stance_duration: The desired stance duration. + duty_factor: The ratio stance_duration / total_gait_cycle. + initial_leg_phase: The desired initial phase [0, 1] of the legs within the + full swing + stance cycle. + contact_detection_force_threshold: The minimal contact force required to + detect if a foot is in contact with the ground. For real robots this + needs to be larger (i.e. 25 for Laikago). + contact_detection_phase_threshold: Updates the state of each leg based on + contact info, when the current normalized phase is greater than this + threshold. This is essential to remove false positives in contact + detection when phase switches. For example, a swing foot at at the + beginning of the gait cycle might be still on the ground. + """ + self._robot = robot + self._stance_duration = stance_duration + self._duty_factor = duty_factor + self._swing_duration = np.array(stance_duration) / np.array( + duty_factor) - np.array(stance_duration) + if len(initial_leg_phase) != len( + list(self._robot.urdf_loader.get_end_effector_id_dict().values())): + raise ValueError( + "The number of leg phases should be the same as number of legs.") + self._initial_leg_phase = initial_leg_phase + + self._initial_leg_state = _DEFAULT_INITIAL_LEG_STATE + self._next_leg_state = [] + # The ratio in cycle is duty factor if initial state of the leg is STANCE, + # and 1 - duty_factory if the initial state of the leg is SWING. + self._initial_state_ratio_in_cycle = [] + for state, duty in zip(self._initial_leg_state, duty_factor): + assert state == gait_generator.LegState.STANCE + self._initial_state_ratio_in_cycle.append(duty) + self._next_leg_state.append(gait_generator.LegState.SWING) + + self._contact_detection_force_threshold = contact_detection_force_threshold + self._contact_detection_phase_threshold = contact_detection_phase_threshold + + # The normalized phase within swing or stance duration. + self._normalized_phase = None + + # The current leg state, when contact is considered. + self._leg_state = None + + # The desired leg state (i.e. SWING or STANCE). + self._desired_leg_state = None + + self.reset(0) + + def reset(self, current_time): + # The normalized phase within swing or stance duration. + self._normalized_phase = np.zeros( + len(list(self._robot.urdf_loader.get_end_effector_id_dict().values()))) + self._leg_state = list(self._initial_leg_state) + self._desired_leg_state = list(self._initial_leg_state) + + @property + def desired_leg_state(self) -> Sequence[gait_generator.LegState]: + """The desired leg SWING/STANCE states. + + Returns: + The SWING/STANCE states for all legs. + + """ + return self._desired_leg_state + + @property + def leg_state(self) -> Sequence[gait_generator.LegState]: + """The leg state after considering contact with ground. + + Returns: + The actual state of each leg after accounting for contacts. + """ + return self._leg_state + + @property + def swing_duration(self) -> Sequence[float]: + return self._swing_duration + + @property + def stance_duration(self) -> Sequence[float]: + return self._stance_duration + + @property + def normalized_phase(self) -> Sequence[float]: + """The phase within the current swing or stance cycle. + + Reflects the leg's phase within the curren swing or stance stage. For + example, at the end of the current swing duration, the phase will + be set to 1 for all swing legs. Same for stance legs. + + Returns: + Normalized leg phase for all legs. + + """ + return self._normalized_phase + + def update(self, current_time): + contact_state = [ + np.linalg.norm(contact_force) > self._contact_detection_force_threshold + for contact_force in self._robot.feet_contact_forces() + ] + + for leg_id in range( + len(list(self._robot.urdf_loader.get_end_effector_id_dict().values()))): + # Here is the explanation behind this logic: We use the phase within the + # full swing/stance cycle to determine if a swing/stance switch occurs + # for a leg. The threshold value is the "initial_state_ratio_in_cycle" as + # explained before. If the current phase is less than the initial state + # ratio, the leg is either in the initial state or has switched back after + # one or more full cycles. + full_cycle_period = ( + self._stance_duration[leg_id] / self._duty_factor[leg_id]) + # To account for the non-zero initial phase, we offset the time duration + # with the effect time contribution from the initial leg phase. + augmented_time = current_time + self._initial_leg_phase[ + leg_id] * full_cycle_period + phase_in_full_cycle = math.fmod(augmented_time, + full_cycle_period) / full_cycle_period + ratio = self._initial_state_ratio_in_cycle[leg_id] + if phase_in_full_cycle < ratio: + self._desired_leg_state[leg_id] = self._initial_leg_state[leg_id] + self._normalized_phase[leg_id] = phase_in_full_cycle / ratio + else: + # A phase switch happens for this leg. + self._desired_leg_state[leg_id] = self._next_leg_state[leg_id] + self._normalized_phase[leg_id] = (phase_in_full_cycle - ratio) / (1 - + ratio) + + self._leg_state[leg_id] = self._desired_leg_state[leg_id] + + # No contact detection at the beginning of each SWING/STANCE phase. + if (self._normalized_phase[leg_id] < + self._contact_detection_phase_threshold): + continue + if (self._leg_state[leg_id] == gait_generator.LegState.SWING and + contact_state[leg_id]): + logging.info("early touch down detected") + self._leg_state[leg_id] = gait_generator.LegState.EARLY_CONTACT + if (self._leg_state[leg_id] == gait_generator.LegState.STANCE and + not contact_state[leg_id]): + self._leg_state[leg_id] = gait_generator.LegState.LOSE_CONTACT diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/raibert_swing_leg_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/raibert_swing_leg_controller.py new file mode 100644 index 000000000..aad4b9b67 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/raibert_swing_leg_controller.py @@ -0,0 +1,242 @@ +"""The swing leg controller class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import math +from typing import Any, Mapping, Sequence, Tuple + +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator as gait_generator_lib +from pybullet_envs.minitaur.agents.baseline_controller import leg_controller + +# The position correction coefficients in Raibert's formula. +_KP = 0.025 + +# At the end of swing, we leave a small clearance to prevent unexpected foot +# collision. +_FOOT_CLEARANCE_M = 0.0 +_DEFAULT_LOCAL_HIP_POSITIONS = ((0.21, -0.1157, 0), (0.21, 0.1157, 0), + (-0.21, -0.1157, 0), (-0.21, 0.1157, 0)) +_DEFAULT_SWING_EASE_UP_PHASE = 1.0 +_DEFAULT_SWING_EASE_UP_PERCENT = 1.0 +_FEED_FORWARD_TORQUES = (-0.7, 0, 0, 0.7, 0, 0, -0.7, 0, 0, 0.7, 0, 0) + + +def _gen_parabola(phase: float, start: float, mid: float, end: float) -> float: + """Gets a point on a parabola y = a x^2 + b x + c. + + The Parabola is determined by three points (0, start), (0.5, mid), (1, end) in + the plane. + + Args: + phase: Normalized to [0, 1]. A point on the x-axis of the parabola. + start: The y value at x == 0. + mid: The y value at x == 0.5. + end: The y value at x == 1. + + Returns: + The y value at x == phase. + """ + mid_phase = 0.5 + delta_1 = mid - start + delta_2 = end - start + delta_3 = mid_phase**2 - mid_phase + coef_a = (delta_1 - delta_2 * mid_phase) / delta_3 + coef_b = (delta_2 * mid_phase**2 - delta_1) / delta_3 + coef_c = start + + return coef_a * phase**2 + coef_b * phase + coef_c + + +def _gen_swing_foot_trajectory(input_phase: float, start_pos: Sequence[float], + end_pos: Sequence[float], ease_up_phase: float, + ease_up_percent: float) -> Tuple[float]: + """Generates the swing trajectory using a parabola. + + Args: + input_phase: the swing/stance phase value between [0, 1]. + start_pos: The foot's position at the beginning of swing cycle. + end_pos: The foot's desired position at the end of swing cycle. + ease_up_phase: Time length for the initial ease up phase dueing swing cycle. + ease_up_percent: Percentage of the swing cycle completed after + ease_up_phase. + + Returns: + The desired foot position at the current phase. + """ + # We augment the swing speed using the below formula. For the first portion of + # the swing cycle (ease_up_phase), the swing leg moves faster and finishes + # ease_up_percent% of the full swing trajectory. The rest of trajectory takes + # the rest of the swing cycle. Intuitely, we want to move the swing foot + # quickly to the target landing location and stay above the ground, in this + # way the control is more robust to perturbations to the body that may cause + # the swing foot to drop onto the ground earlier than expected. This is a + # common practice similar to the MIT cheetah and Marc Raibert's original + # controllers. + assert 0 <= ease_up_percent <= 1 + assert 0 <= ease_up_phase <= 1 + phase = input_phase + if input_phase <= ease_up_phase: + phase = ease_up_percent * math.sin(input_phase / + (2 * ease_up_phase) * math.pi) + else: + phase = ease_up_percent + (input_phase - ease_up_phase) * ( + 1 - ease_up_percent) / (1 - ease_up_phase) + + x = (1 - phase) * start_pos[0] + phase * end_pos[0] + y = (1 - phase) * start_pos[1] + phase * end_pos[1] + max_clearance = 0.1 + mid = max(end_pos[2], start_pos[2]) + max_clearance + z = _gen_parabola(phase, start_pos[2], mid, end_pos[2]) + + # PyType detects the wrong return type here. + return (x, y, z) # pytype: disable=bad-return-type + + +@gin.configurable +class RaibertSwingLegController(leg_controller.LegController): + """Controls the swing leg position using Raibert's formula. + + For details, please refer to chapter 2 in "Legged robbots that balance" by + Marc Raibert. The key idea is to stablize the swing foot's location based on + the CoM moving speed. + + """ + + def __init__( + self, + robot: Any, + gait_generator: Any, + state_estimator: Any, + desired_speed: Tuple[float] = (0, 0), + desired_twisting_speed: float = 0, + desired_height: float = 0.45, + foot_clearance: float = _FOOT_CLEARANCE_M, + local_hip_positions: Tuple[Tuple[float]] = _DEFAULT_LOCAL_HIP_POSITIONS, + ease_up_phase: float = _DEFAULT_SWING_EASE_UP_PHASE, + ease_up_percent: float = _DEFAULT_SWING_EASE_UP_PERCENT, + feed_forward_torques: Sequence[float] = _FEED_FORWARD_TORQUES + ): + """Initializes the class. + + Args: + robot: A robot instance. + gait_generator: Generates the stance/swing pattern. + state_estimator: Estiamtes the CoM speeds. + desired_speed: Behavior parameters. X-Y speed. + desired_twisting_speed: Behavior control parameters. + desired_height: Desired standing height. + foot_clearance: The foot clearance on the ground at the end of the swing + cycle. + local_hip_positions: Positions of the robot's hips in local frames. + ease_up_phase: Time length for the initial ease up phase dueing swing + cycle. + ease_up_percent: Percentage of the swing cycle completed after + ease_up_phase. + feed_forward_torques: A feed-forward torque applied to the actuators on + the swing legs (e.g. for gravity compensation). + """ + self._robot = robot + self._state_estimator = state_estimator + self._gait_generator = gait_generator + self._last_leg_state = gait_generator.desired_leg_state + self.desired_speed = np.array((desired_speed[0], desired_speed[1], 0)) + self.desired_twisting_speed = desired_twisting_speed + self._desired_height = np.array((0, 0, desired_height - foot_clearance)) + self._local_hip_positions = local_hip_positions + self._ease_up_phase = ease_up_phase + self._ease_up_percent = ease_up_percent + self._feed_forward_torques = feed_forward_torques + + self._joint_angles = None + self._phase_switch_foot_local_position = None + self.reset(0) + + def reset(self, current_time: float) -> None: + """Called during the start of a swing cycle. + + Args: + current_time: The wall time in seconds. + """ + del current_time + self._last_leg_state = self._gait_generator.desired_leg_state + self._phase_switch_foot_local_position = (self._robot.foot_positions()) + self._joint_angles = {} + + def update(self, current_time: float) -> None: + """Called at each control step. + + Args: + current_time: The wall time in seconds. + """ + del current_time + new_leg_state = self._gait_generator.desired_leg_state + + # Detects phase switch for each leg so we can remember the feet position at + # the beginning of the swing phase. + for leg_id, state in enumerate(new_leg_state): + if (state == gait_generator_lib.LegState.SWING and + state != self._last_leg_state[leg_id]): + self._phase_switch_foot_local_position[leg_id] = ( + self._robot.foot_positions()[leg_id]) + + self._last_leg_state = copy.deepcopy(new_leg_state) + + def get_action(self) -> Mapping[Any, Any]: + com_velocity = self._state_estimator.com_velocity_body_yaw_aligned_frame + com_velocity = np.array((com_velocity[0], com_velocity[1], 0)) + + _, _, yaw_dot = self._robot.base_roll_pitch_yaw_rate + + local_toe_positions = np.array(self._robot.foot_positions()) + + for leg_id, leg_state in enumerate(self._gait_generator.leg_state): + if (leg_state == gait_generator_lib.LegState.STANCE or + leg_state == gait_generator_lib.LegState.EARLY_CONTACT): + continue + + # For now we did not consider the body pitch/roll and all calculation is + # in the body frame. TODO(b/143378213): Calculate the foot_target_position + # in world farme and then project back to calculate the joint angles. + hip_offset = self._local_hip_positions[leg_id] + twisting_vector = np.array((-hip_offset[1], hip_offset[0], 0)) + hip_horizontal_velocity = com_velocity + yaw_dot * twisting_vector + target_hip_horizontal_velocity = ( + self.desired_speed + self.desired_twisting_speed * twisting_vector) + + foot_target_position = ( + hip_horizontal_velocity * + self._gait_generator.swing_duration[leg_id] / 2 - _KP * + (target_hip_horizontal_velocity - hip_horizontal_velocity) + ) - self._desired_height + np.array((hip_offset[0], hip_offset[1], 0)) + + foot_position = _gen_swing_foot_trajectory( + self._gait_generator.normalized_phase[leg_id], + self._phase_switch_foot_local_position[leg_id], foot_target_position, + self._ease_up_phase, self._ease_up_percent) + + local_toe_positions[leg_id] = foot_position + joint_ids, joint_angles = self._robot.motor_angles_from_foot_positions( + local_toe_positions, position_in_world_frame=False) + # Update the stored joint angles as needed. + motors_per_leg = len(joint_ids) // len(local_toe_positions) + for joint_id, joint_angle in zip(joint_ids, joint_angles): + self._joint_angles[joint_id] = (joint_angle, joint_id // motors_per_leg) + + action = {} + kps, kds = self._robot.motor_model.get_motor_gains() + + for joint_id, joint_angle_leg_id in self._joint_angles.items(): + leg_id = joint_angle_leg_id[1] + if self._gait_generator.leg_state[ + leg_id] == gait_generator_lib.LegState.SWING: + # This is a hybrid action for PD control. + action[joint_id] = (joint_angle_leg_id[0], kps[joint_id], 0, + kds[joint_id], self._feed_forward_torques[joint_id]) + + return action diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/state_estimator.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/state_estimator.py new file mode 100644 index 000000000..6b12a05ad --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/state_estimator.py @@ -0,0 +1,21 @@ +"""State estimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc + + +class StateEstimatorBase(object): # pytype: disable=ignored-metaclass + """Estimates the unmeasurable state of the robot.""" + + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def reset(self, current_time): + pass + + @abc.abstractmethod + def update(self, current_time): + pass diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/static_gait_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/static_gait_controller.py new file mode 100644 index 000000000..eb4ccdd1c --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/static_gait_controller.py @@ -0,0 +1,63 @@ +# Lint as: python3 +"""A static gait controller for a quadruped robot. Experimental code.""" + +import gin +import numpy as np +from pybullet_envs.minitaur.agents.baseline_controller import foot_stepper + +toe_pos_local_ref = np.array([[0.1478, -0.11459, -0.45576], + [0.1478, 0.11688, -0.45576], + [-0.2895, -0.11459, -0.45576], + [-0.2895, 0.11688, -0.45576]]) + + +@gin.configurable +class StaticGaitController(object): + """A static gait controller for a quadruped robot.""" + + def __init__(self, robot): + self._robot = robot + self._toe_ids = tuple(robot.urdf_loader.get_end_effector_id_dict().values()) + self._wait_count = 0 + self._stepper = foot_stepper.FootStepper(self._robot.pybullet_client, + self._toe_ids, toe_pos_local_ref) + + def act(self, observation): + """Computes actions based on observations.""" + del observation + p = self._robot.pybullet_client + quadruped = self._robot.robot_id + step_input = foot_stepper.StepInput() + ls = p.getLinkStates( + quadruped, self._toe_ids, computeForwardKinematics=True) + toe_pos_world = np.array([ls[0][0], ls[1][0], ls[2][0], ls[3][0]]) + base_com_pos, base_com_orn = p.getBasePositionAndOrientation(quadruped) + new_pos_world = np.array([0, 0, 0]) + + if self._stepper.is_com_stable() and not self._stepper.move_swing_foot: + self._wait_count += 1 + if self._wait_count == 20: + self._stepper.next_foot() + if self._wait_count > 50: + self._wait_count = 0 + step_dist = 0.15 + print("time {}, make a step of {}".format( + self._robot.GetTimeSinceReset(), step_dist)) + new_pos_local = self._stepper.get_reference_pos_swing_foot() + new_pos_local[0] += step_dist + new_pos_world, _ = p.multiplyTransforms(base_com_pos, base_com_orn, + new_pos_local, [0, 0, 0, 1]) + self._stepper.swing_foot() + + step_input.new_pos_world = new_pos_world + step_input.base_com_pos = base_com_pos + step_input.base_com_orn = base_com_orn + step_input.toe_pos_world = toe_pos_world + step_input.dt = 1.0 / 250 + step_output = self._stepper.update(step_input) + + # Finds joint poses to achieve toePosWorld + desired_joint_angles = self._robot.motor_angles_from_foot_positions( + foot_positions=step_output.new_toe_pos_world, + position_in_world_frame=True)[1] + return desired_joint_angles diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/time_based_moving_window_filter.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/time_based_moving_window_filter.py new file mode 100644 index 000000000..42868795e --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/time_based_moving_window_filter.py @@ -0,0 +1,45 @@ +"""A moving-window filter for smoothing the signals within certain time interval.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import gin +import numpy as np + + +@gin.configurable +class TimeBasedMovingWindowFilter: + """A moving-window filter for smoothing the signals within certain time interval.""" + + def __init__( + self, + filter_window: float = 0.1, + ): + """Initializes the class. + + Args: + filter_window: The filtering window (in time) used to smooth the input + signal. + """ + self._filter_window = filter_window + self.reset() + + def reset(self): + self._timestamp_buffer = [] + self._value_buffer = [] + + def calculate_average(self, new_value, timestamp): + """Compute the filtered signals based on the time-based moving window.""" + self._timestamp_buffer.append(timestamp) + self._value_buffer.append(new_value) + + while len(self._value_buffer) > 1: + if self._timestamp_buffer[ + 0] < timestamp - self._filter_window: + self._timestamp_buffer.pop(0) + self._value_buffer.pop(0) + else: + break + return np.mean(self._value_buffer, axis=0) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/torque_stance_leg_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/torque_stance_leg_controller.py new file mode 100644 index 000000000..87ebed458 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/torque_stance_leg_controller.py @@ -0,0 +1,229 @@ +# Lint as: python3 +"""A torque based stance controller framework.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import logging +from typing import Any, Sequence, Tuple + +import gin +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator as gait_generator_lib +from pybullet_envs.minitaur.agents.baseline_controller import leg_controller +#from pybullet_envs.minitaur.agents.baseline_controller.convex_mpc.python import convex_mpc +#from google3.util.task.python import error + +try: + import mpc_osqp as convex_mpc # pytype: disable=import-error +except: #pylint: disable=W0702 + print("You need to install motion_imitation") + print("or use pip3 install motion_imitation --user") + print("see also https://github.com/google-research/motion_imitation") + import sys + sys.exit() + + +_FORCE_DIMENSION = 3 +# The QP weights in the convex MPC formulation. See the MIT paper for details: +# https://ieeexplore.ieee.org/document/8594448/ +# Intuitively, this is the weights of each state dimension when tracking a +# desired CoM trajectory. The full CoM state is represented by +# (roll_pitch_yaw, position, angular_velocity, velocity, gravity_place_holder). +_MPC_WEIGHTS = (5, 5, 0.2, 0, 0, 10, 0.5, 0.5, 0.2, 0.2, 0.2, 0.1, 0) +_PLANNING_HORIZON_STEPS = 10 +_PLANNING_TIMESTEP = 0.025 +#_MPC_CONSTRUCTOR = functools.partial( +# convex_mpc.ConvexMpc, qp_solver_name=convex_mpc.QPSolverName.QPOASES) + + +@gin.configurable +class TorqueStanceLegController(leg_controller.LegController): + """A torque based stance leg controller framework. + + Takes in high level parameters like walking speed and turning speed, and + generates necessary the torques for stance legs. + """ + + def __init__( + self, + robot: Any, + gait_generator: Any, + state_estimator: Any, + desired_speed: Tuple[float] = (0, 0), + desired_twisting_speed: float = 0, + desired_roll_pitch: Tuple[float] = (0, 0), + desired_body_height: float = 0.45, + body_mass: float = 220 / 9.8, + body_inertia: Tuple[float] = (0.183375, 0, 0, 0, 0.6267, 0, 0, 0, + 0.636175), + num_legs: int = 4, + friction_coeffs: Sequence[float] = (0.5, 0.5, 0.5, 0.5), + qp_weights: Sequence[float] = _MPC_WEIGHTS, + planning_horizon: int = _PLANNING_HORIZON_STEPS, + planning_timestep: int = _PLANNING_TIMESTEP, + ): + """Initializes the class. + + Tracks the desired position/velocity of the robot by computing proper joint + torques using MPC module. + + Args: + robot: A robot instance. + gait_generator: Used to query the locomotion phase and leg states. + state_estimator: Estimate the robot states (e.g. CoM velocity). + desired_speed: desired CoM speed in x-y plane. + desired_twisting_speed: desired CoM rotating speed in z direction. + desired_roll_pitch: desired CoM roll and pitch. + desired_body_height: The standing height of the robot. + body_mass: The total mass of the robot. + body_inertia: The inertia matrix in the body principle frame. We assume + the body principle coordinate frame has x-forward and z-up. + num_legs: The number of legs used for force planning. + friction_coeffs: The friction coeffs on the contact surfaces. + qp_weights: The weights used in solving the QP problem. + planning_horizon: Number of steps to roll-out in the QP formulation. + planning_timestep: Timestep between each step in the QP formulation. + """ + + self._robot = robot + self._gait_generator = gait_generator + self._state_estimator = state_estimator + self._desired_speed = desired_speed + self._desired_twisting_speed = desired_twisting_speed + self._desired_roll_pitch = desired_roll_pitch + self._desired_body_height = desired_body_height + self._body_mass = body_mass + self._num_legs = num_legs + self._friction_coeffs = np.array(friction_coeffs) + self._qp_solver_fail = False + self._com_estimate_leg_indices = None + qp_solver = convex_mpc.QPOASES #convex_mpc.OSQP # + + body_inertia_list = list(body_inertia) + weights_list = list(qp_weights) + + self._mpc = convex_mpc.ConvexMpc( + body_mass, + body_inertia_list, + self._num_legs, + planning_horizon, + planning_timestep, + weights_list, + 1e-6, + qp_solver + ) + + + def reset(self, current_time): + del current_time + self._qp_solver_fail = False + self._com_estimate_leg_indices = None + + def update(self, current_time): + del current_time + + def get_action(self): + """Computes the torque for stance legs.""" + desired_com_position = np.array((0., 0., self._desired_body_height), + dtype=np.float64) + desired_com_velocity = np.array( + (self.desired_speed[0], self.desired_speed[1], 0.), dtype=np.float64) + desired_com_roll_pitch_yaw = np.array( + (self.desired_roll_pitch[0], self.desired_roll_pitch[1], 0.), + dtype=np.float64) + desired_com_angular_velocity = np.array( + (0., 0., self.desired_twisting_speed), dtype=np.float64) + foot_contact_state = np.array( + [(leg_state == gait_generator_lib.LegState.STANCE or + leg_state == gait_generator_lib.LegState.EARLY_CONTACT) + for leg_state in self._gait_generator.desired_leg_state], + dtype=np.int32) + + # We use the body yaw aligned world frame for MPC computation. + com_roll_pitch_yaw = np.array( + self._robot.base_roll_pitch_yaw, dtype=np.float64) + com_roll_pitch_yaw[2] = 0 + #try: + estimated_com_position = np.array(()) + if hasattr(self._state_estimator, "estimated_com_height"): + estimated_com_position = np.array( + (0, 0, self._state_estimator.estimated_com_height)) + try: + predicted_contact_forces = self._mpc.compute_contact_forces( + estimated_com_position, #com_position + np.asarray(self._state_estimator.com_velocity_body_yaw_aligned_frame, + dtype=np.float64), #com_velocity + np.array(com_roll_pitch_yaw, dtype=np.float64), #com_roll_pitch_yaw + # Angular velocity in the yaw aligned world frame is actually different + # from rpy rate. We use it here as a simple approximation. + np.asarray(self._robot.base_roll_pitch_yaw_rate, + dtype=np.float64), #com_angular_velocity + foot_contact_state, #foot_contact_states + np.array(self._robot.foot_positions( + position_in_world_frame=False).flatten(), + dtype=np.float64), #foot_positions_base_frame + self._friction_coeffs, #foot_friction_coeffs + desired_com_position, #desired_com_position + desired_com_velocity, #desired_com_velocity + desired_com_roll_pitch_yaw, #desired_com_roll_pitch_yaw + desired_com_angular_velocity #desired_com_angular_velocity + ) + except:# error.StatusNotOk as e: + logging.error("Error in Torque Stance Leg")#e.message) + self._qp_solver_fail = True + predicted_contact_forces = np.zeros(self._num_legs * _FORCE_DIMENSION) + + contact_forces = {} + for i in range(self._num_legs): + contact_forces[i] = np.array( + predicted_contact_forces[i * _FORCE_DIMENSION:(i + 1) * + _FORCE_DIMENSION]) + + _, kds = self._robot.motor_model.get_motor_gains() + action = {} + for leg_id, force in contact_forces.items(): + motor_torques = self._robot.map_contact_force_to_joint_torques( + leg_id, force) + for joint_id, torque in motor_torques.items(): + action[joint_id] = (0, 0, 0, kds[joint_id], torque) + return action + + @property + def qp_solver_fail(self): + return self._qp_solver_fail + + @property + def desired_speed(self): + return self._desired_speed + + @desired_speed.setter + def desired_speed(self, speed): + self._desired_speed = speed + + @property + def desired_twisting_speed(self): + return self._desired_twisting_speed + + @desired_twisting_speed.setter + def desired_twisting_speed(self, twisting_speed): + self._desired_twisting_speed = twisting_speed + + @property + def desired_roll_pitch(self): + return self._desired_roll_pitch + + @desired_roll_pitch.setter + def desired_roll_pitch(self, roll_pitch): + self._desired_roll_pitch = roll_pitch + + @property + def desired_body_height(self): + return self._desired_body_height + + @desired_body_height.setter + def desired_body_height(self, body_height): + self._desired_body_height = body_height diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/controller_simple.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/controller_simple.py new file mode 100644 index 000000000..746e4cfd5 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/controller_simple.py @@ -0,0 +1,87 @@ +"""Asymmetric sine controller for quadruped locomotion. + +Asymmetric sine uses cosine and sine waves to generate swinging and extension +for leg motion. It's asymmetric because sine waves are split into two phases +(swing forward and stance) and these phases have different frequencies according +to what proportion of a period will be spend on swinging forward forward +swinging backwards. In addition, the sine wave for extension has different +amplitudes during these two phases. +""" +import math + +TWO_PI = 2 * math.pi +_DEFAULT_LEG_AMPLITUDE_EXTENSION = -0.02 +_DEFAULT_LEG_AMPLITUDE_SWING = 0.5 +_DEFAULT_LEG_AMPLITUDE_LIFT = 0.9 +_DEFAULT_WALKING_HEIGHT = 0.0 +_DEFAULT_LEG_CENTER_SWING = 0.0 +_DELTA_CENTER_EXTENSION_CAP = 1 +_DELTA_INTENSITY_CAP = 0.1 + + +class SimpleLegController(object): + """Controller that gives swing and extension based on phase and parameters. + + + The controller returns the swing-extend pair based on a parameterized + ellipsoid trajectory that depends on center of motion, amplitude and phase. + The parameters are + amplitude_extension: Amplitude for extension during stance (phase < pi). + amplitude_lift: Amplitude for extension during swing (phase > pi). + amplitude_swing: Amplitude for swing. + center_extension: The value extension signal oscillates around. + center_swing: The value swing signal oscillates around. + intensity: A coefficient that scales the motion of the legs. + The formula to calculate motion and more detailed information about these + parameters can be found at go/pmtg-refactored. + """ + + def __init__(self, init_phase=0): + self.amplitude_extension = _DEFAULT_LEG_AMPLITUDE_EXTENSION + self.amplitude_swing = _DEFAULT_LEG_AMPLITUDE_SWING + self.amplitude_lift = _DEFAULT_LEG_AMPLITUDE_LIFT + self.center_extension = _DEFAULT_WALKING_HEIGHT + self.center_swing = _DEFAULT_LEG_CENTER_SWING + self.intensity = 1.0 + self._init_phase = init_phase + self.phase = init_phase + self.phase_offset = 0 + + def reset(self): + self.phase = self._init_phase + + def get_swing_extend(self): + """Returns the swing and extend parameters for the leg. + + Returns: + swing: Desired swing of the leg. + extend: Desired extension amount of the leg. + """ + + # Increase default extension by the extra extension scaled by intensity. + # Extend reduces to default center extension when intensity goes to 0, + # because we prefer the legs to stay at walking height when intensity is + # 0. + amplitude_extension = self.amplitude_extension + # The leg is in swing phase when phase > pi. + if self.phase > math.pi: + amplitude_extension = self.amplitude_lift + extend = self.center_extension + ( + amplitude_extension * math.sin(self.phase)) * self.intensity + # Calculate the swing based on the signal and scale it with intensity. + # Swing reduces to 0 when intensity goes to 0, because we would prefer the + # legs to stay neutral (standing position instead of center swing) when + # intensity is 0. + swing = self.center_swing + self.amplitude_swing * math.cos(self.phase) + swing *= self.intensity + + return swing, extend + + def adjust_center_extension(self, target_center_extension): + delta = min(_DELTA_CENTER_EXTENSION_CAP, + target_center_extension - self.center_extension) + self.center_extension += delta + + def adjust_intensity(self, target_intensity): + delta = min(_DELTA_INTENSITY_CAP, target_intensity - self.intensity) + self.intensity += delta diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_inplace.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_inplace.py new file mode 100644 index 000000000..b07048e75 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_inplace.py @@ -0,0 +1,57 @@ +"""Trajectory Generator for in-place stepping motion for quadruped robot.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import numpy as np + +TWO_PI = 2 * math.pi + + +def _get_actions_asymmetric_sine(phase, tg_params): + """Returns the leg extension given current phase of TG and parameters. + + Args: + phase: a number in [0, 2pi) representing current leg phase + tg_params: a dictionary of tg parameters: + stance_lift_cutoff -- switches the TG between stance (phase < cutoff) and + lift (phase > cutoff) phase + amplitude_swing -- amplitude in swing phase + amplitude_lift -- amplitude in lift phase + center_extension -- center of leg extension + """ + stance_lift_cutoff = tg_params['stance_lift_cutoff'] + a_prime = np.where(phase < stance_lift_cutoff, tg_params['amplitude_stance'], + tg_params['amplitude_lift']) + scaled_phase = np.where( + phase > stance_lift_cutoff, np.pi + (phase - stance_lift_cutoff) / + (TWO_PI - stance_lift_cutoff) * np.pi, phase / stance_lift_cutoff * np.pi) + return tg_params['center_extension'] + a_prime * np.sin(scaled_phase) + + +def step(current_phases, leg_frequencies, dt, tg_params): + """Steps forward the in-place trajectory generator. + + Args: + current_phases: phases of each leg. + leg_frequencies: the frequency to proceed the phase of each leg. + dt: amount of time (sec) between consecutive time steps. + tg_params: a set of parameters for trajectory generator, see the docstring + of "_get_actions_asymmetric_sine" for details. + + Returns: + actions: leg swing/extensions as output by the trajectory generator. + new_state: new swing/extension. + """ + new_phases = np.fmod(current_phases + TWO_PI * leg_frequencies * dt, TWO_PI) + extensions = [] + for leg_id in range(4): + extensions.append( + _get_actions_asymmetric_sine(new_phases[..., leg_id], tg_params)) + return new_phases, extensions + + +def reset(): + return np.array([0, np.pi * 0.5, np.pi, np.pi * 1.5]) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_simple.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_simple.py new file mode 100644 index 000000000..c63884d81 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/tg_simple.py @@ -0,0 +1,400 @@ +"""Trajectory Generator generates walking leg motions for a quadruped robot. + +Trajectory Generator (TG) has an internal state (phase) and generates +walking-like motion for 8 motors of minitaur quadruped robot based on +parameters +such as: + - delta time to progress the TG's internal state. + - intensity to control amount of movement (stride length and lift of the legs). + - waking height to control the average extension of the legs. + +Each time step() is called, the internal state is progressed and 8 motor +positions are generated. This TG uses the open-loop SineController class to +provide leg positions. It is mainly a wrapper for ability to modulating the +time +and other parameters of the SineController. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import numpy as np +import gin +from pybullet_envs.minitaur.agents.trajectory_generator import controller_simple + +PHASE_LOWER_BOUND = 0.0 +PHASE_UPPER_BOUND = 1.0 +WALK_HEIGHT_LOWER_BOUND = -0.5 +WALK_HEIGHT_UPPER_BOUND = 1.0 +INTENSITY_LOWER_BOUND = 0.0 +INTENSITY_UPPER_BOUND = 1.5 +_SWING_STANCE_LOWER_BOUND = 0.2 +_SWING_STANCE_UPPER_BOUND = 5.0 +_DELTA_SWING_STANCE_CAP = 0.4 +_TWO_PI = math.pi * 2.0 +_LEG_COUPLING_DICT = { + "null": [], + # All the legs are coupled. + "all coupled": [0, 0, 0, 0], + # Front legs and back legs are coupled separately. + "front back": [0, 1, 0, 1], + # Left legs and right legs are coupled separately. + "left right": [0, 0, 1, 1], + # Diagonal legs are coupled (i.e. trottting). + "diagonal": [0, 1, 1, 0], + # Each leg is indepenent. + "decoupled": [0, 1, 2, 3] +} + + +@gin.configurable +class TgSimple(object): + """TgSimple class is a simplified trajectory generator for quadruped walking. + + It returns 8 actions for quadruped slow walking behavior + based on the parameters provided such as intensity, walking height and delta + time. It returns its internal phase as information. + """ + + def __init__(self, + walk_height_lower_bound=WALK_HEIGHT_LOWER_BOUND, + walk_height_upper_bound=WALK_HEIGHT_UPPER_BOUND, + intensity_lower_bound=INTENSITY_LOWER_BOUND, + intensity_upper_bound=INTENSITY_UPPER_BOUND, + swing_stance_lower_bound=_SWING_STANCE_LOWER_BOUND, + swing_stance_upper_bound=_SWING_STANCE_UPPER_BOUND, + integrator_coupling_mode="all coupled", + walk_height_coupling_mode="all coupled", + variable_swing_stance_ratio=False, + swing_stance_ratio=1.0, + init_leg_phase_offsets=None): + """Initialize the trajectory generator with a controller. + + For trajectory generator, we create an asymmetric sine controller with + parameters that was previously optimized as an open-loop controller. + + Args: + walk_height_lower_bound: Lower bound for walking height which sets the + default leg extension of the gait. Unit is rad, -0.5 by default. + walk_height_upper_bound: Lower bound for walking height which sets the + default leg extension of the gait. Unit is rad, 1.0 by default. + intensity_lower_bound: The upper bound for intensity of the trajectory + generator. It can be used to limit the leg movement. + intensity_upper_bound: The upper bound for intensity of the trajectory + generator. It can be used to limit the leg movement. + swing_stance_lower_bound: Lower bound for the swing vs stance ratio + parameter. Default value is 0.2. + swing_stance_upper_bound: Upper bound for the swing vs stance ratio + parameter. Default value is 0.2. + integrator_coupling_mode: How the legs should be coupled for integrators. + walk_height_coupling_mode: The same coupling mode used for walking + heights for the legs. + variable_swing_stance_ratio: A boolean to indicate if the swing stance + ratio can change per time step or not. + swing_stance_ratio: Time taken by swing phase vs stance phase. This is + only relevant if variable_swing_stance_ratio is False. + init_leg_phase_offsets: The initial phases of the legs. A list of 4 + variables within [0,1). The order is front-left, rear-left, front-right + and rear-right. + + Raises: + ValueError: If parameters are not valid values. + """ + self._walk_height_lower_bound = walk_height_lower_bound + self._walk_height_upper_bound = walk_height_upper_bound + self._intensity_lower_bound = intensity_lower_bound + self._intensity_upper_bound = intensity_upper_bound + self._swing_stance_lower_bound = swing_stance_lower_bound + self._swing_stance_upper_bound = swing_stance_upper_bound + if not init_leg_phase_offsets: + init_leg_phase_offsets = [0, 0.25, 0.5, 0.75] + if len(init_leg_phase_offsets) != 4: + raise ValueError("The number of leg phase offsets is not equal to 4.") + if min(init_leg_phase_offsets) < 0 or max(init_leg_phase_offsets) >= 1: + raise ValueError("Leg phase offsets are not within [0,1)") + self._legs = [] + for period in init_leg_phase_offsets: + init_phase = period * 2 * math.pi + self._legs.append(controller_simple.SimpleLegController(init_phase)) + + if integrator_coupling_mode not in _LEG_COUPLING_DICT: + raise ValueError("Invalid integrator_coupling_mode.") + if walk_height_coupling_mode not in _LEG_COUPLING_DICT: + raise ValueError("Invalid walk_height_coupling_mode.") + + # Set the phase couplings and build a list of legs per phase coupling. + self._integrator_id_per_leg = _LEG_COUPLING_DICT[integrator_coupling_mode] + self._num_integrators = max( + self._integrator_id_per_leg) + 1 if self._integrator_id_per_leg else 0 + self._legs_per_integrator_id = [[], [], [], []] + for idx, phase_id in enumerate(self._integrator_id_per_leg): + self._legs_per_integrator_id[phase_id].append(self._legs[idx]) + + # For each integrator coupling, create a integrator unit. + # For each leg controlled by that phase generator, mark the phase offset. + self._integrator_units = [] + for legs_per_integrator in self._legs_per_integrator_id: + if legs_per_integrator: + circular_integrator = CircularAsymmetricalIntegratorUnit( + legs_per_integrator[0].phase) + self._integrator_units.append(circular_integrator) + for leg in legs_per_integrator: + leg.phase_offset = leg.phase - circular_integrator.phase + + # Set the walking heights couplings. + self._walk_height_id_per_leg = _LEG_COUPLING_DICT[walk_height_coupling_mode] + self._num_walk_heights = max( + self._walk_height_id_per_leg) + 1 if self._walk_height_id_per_leg else 0 + self._variable_swing_stance_ratio = variable_swing_stance_ratio + self._swing_stance_ratio = swing_stance_ratio + + def reset(self): + """Resets leg phase offsets to their initial values.""" + for leg in self._legs: + leg.reset() + for circular_integrator in self._integrator_units: + circular_integrator.reset() + + def get_parameter_bounds(self): + """Lower and upper bounds for the parameters generator's parameters. + + Returns: + 2-tuple of: + - Lower bounds for the parameters such as intensity, walking height and + lift fraction. + - Upper bounds for the same parameters. + """ + lower_bounds = [self._intensity_lower_bound] + upper_bounds = [self._intensity_upper_bound] + lower_bounds += [self._walk_height_lower_bound] * self._num_walk_heights + upper_bounds += [self._walk_height_upper_bound] * self._num_walk_heights + lower_bounds += [self._swing_stance_lower_bound + ] * self._variable_swing_stance_ratio + upper_bounds += [self._swing_stance_upper_bound + ] * self._variable_swing_stance_ratio + + return lower_bounds, upper_bounds + + def get_actions(self, delta_real_time, tg_params): + """Get actions for 8 motors after increasing the phase delta_time. + + Args: + delta_real_time: Time in seconds that have actually passed since the last + step of the trajectory generator. + tg_params: An ndarray of the parameters for generating the trajectory. The + parameters must be in the correct order (time_scale, intensity, + walking_height, and swing vs stance) + + Raises: + ValueError: In case the input dimension does not match expected. + Returns: + The rotations for all the 8 motors for this time step + returned in an array [front_left_motor_1, front_left_motor_2, etc]. + """ + speeds, intensity, heights, swing_stance_ratio = self._process_tg_params( + tg_params) + # Adjust the swing stance ratio of the controller (used for all four legs). + if swing_stance_ratio: + self.adjust_swing_stance_ratio(swing_stance_ratio) + # Adjust the walking height, intensity and swing vs stance of the legs. + for idx, leg in enumerate(self._legs): + leg.adjust_intensity(intensity) + if heights: + leg.adjust_center_extension(heights[self._walk_height_id_per_leg[idx]]) + + # Progress all the phase generators based on delta time. + for idx, integrator_unit in enumerate(self._integrator_units): + integrator_unit.progress_phase(speeds[idx] * delta_real_time, + self._swing_stance_ratio) + + # Set the phases for the legs based on their offsets with phase generators. + for phase_id, leg_list in enumerate(self._legs_per_integrator_id): + for leg in leg_list: + delta_period = leg.phase_offset / (2.0 * math.pi) + leg.phase = self._integrator_units[phase_id].calculate_progressed_phase( + delta_period, self._swing_stance_ratio) + + # Calculate swingextend and convert it to the motor rotations. + actions = [] + for idx, leg in enumerate(self._legs): + swing, extend = leg.get_swing_extend() + actions.extend([swing, extend]) + return actions + + def _process_tg_params(self, tg_params): + """Process the trajectory generator parameters and split them. + + Args: + tg_params: A list consisting of time_scales, intensity, walking_heights, + swing_stance_ratio. The size depends on the configuration and inital + flags. + + Returns: + time_scales: A list of multipliers of delta time (one per integrator). + intensity: Intensity of the trajectory generator (one variable). + walking_heights: Walking heights used for the legs. The length depends on + the coupling between the legs selected at the initialization. + swing_stance_ratio: The ratio of the speed of the leg during swing stance + vs stance phase. + """ + + # Check if the given input's dimension matches the expectation considering + # the number of parameters the trajectory generator uses. + if isinstance(tg_params, np.ndarray): + tg_params = tg_params.tolist() + expected_action_dim = 1 + self._num_integrators + self._num_walk_heights + if self._variable_swing_stance_ratio: + expected_action_dim += 1 + if len(tg_params) != expected_action_dim: + raise ValueError( + "Action dimension does not match the expectation {} vs {}".format( + len(tg_params), expected_action_dim)) + # Split input into different parts based on type. The order must match the + # order given by the order in get_parameter_bounds + time_scales = tg_params[0:self._num_integrators] + intensity = tg_params[self._num_integrators] + walking_heights = tg_params[(self._num_integrators + 1):( + 1 + self._num_integrators + self._num_walk_heights)] + swing_stance_ratio = None + if self._variable_swing_stance_ratio: + swing_stance_ratio = tg_params[1 + self._num_integrators + + self._num_walk_heights] + + return time_scales, intensity, walking_heights, swing_stance_ratio + + def get_state(self): + """Returns a list of floats representing the phase of the controller. + + The phase of the controller is composed of the phases of the integrators. + For each integrator, the phase is composed of 2 floats that represents the + sine and cosine of the phase of that integrator. + + Returns: + List containing sine and cosine of the phases of all the integrators. + """ + return [x for y in self._integrator_units for x in y.get_state()] + + def get_state_lower_bounds(self): + """Lower bounds for the internal state. + + Returns: + The list containing the lower bounds. + """ + return [PHASE_LOWER_BOUND] * 2 * self._num_integrators + + def get_state_upper_bounds(self): + """Upper bounds for the internal state. + + Returns: + The list containing the upper bounds. + """ + return [PHASE_UPPER_BOUND] * 2 * self._num_integrators + + def adjust_swing_stance_ratio(self, target_swing_stance_ratio): + """Adjust the parameter swing_stance_ratio towards a given target value. + + Args: + target_swing_stance_ratio: The target value for the ratio between swing + and stance phases. + """ + delta = min(_DELTA_SWING_STANCE_CAP, + target_swing_stance_ratio - self._swing_stance_ratio) + self._swing_stance_ratio += delta + + @property + def num_integrators(self): + """Gets the number of integrators used based on coupling mode.""" + return self._num_integrators + + +class CircularAsymmetricalIntegratorUnit(object): + """A circular integrator with asymmetry between first and second half. + + An integrator is a memory unit that accumulates the given parameter at every + time step. + A circular integrator is when the integrator cycles within [0,2pi]. + The phase of a circular integrator indicates the accumulated number and it is + stored as fmod of 2Pi. + Asymmetrical circular integrator has a further characteristic where it + distinguishes between the first half of the period vs the second half. It + allows the integrator to move at different speeds during these two periods. + From a locomotion perspective these two halves of the period can be considered + as swing and stance phases. The speed difference is calculated using the + variable swing_stance_ratio provided at every time step. + CircularAsymmetricalIntegratorUnit can be used to control one or multiple legs + depending on the preference. If more than one leg is assigned to a single unit + the other legs are calculated based on their initial phase difference. + """ + + def __init__(self, init_phase=0): + self._init_phase = init_phase + self.reset() + + def reset(self): + self.phase = self._init_phase + + def calculate_progressed_phase(self, delta_period, swing_stance_speed_ratio): + """Calculate a hypotethical phase based on the current phase and args. + + This is used to both calculate the new phase, as well as the current phase + of the other legs with a given offset of delta_period. + + Args: + delta_period: The fraction of the period to add to the current phase of + the integrator. If set to 1, the integrator will accomplish one full + period and return the same phase. The calculated phase will depend on + the current phase (if it is in first half vs second half) and swing vs + stance speed ratio. + swing_stance_speed_ratio: The ratio of the speed of the phase when it is + in swing (second half) vs stance (first half). Set to 1.0 by default, + making it symettric, same as a classical integrator. + + Returns: + The new phase between 0 and 2 * pi. + """ + stance_speed_coef = ( + swing_stance_speed_ratio + 1) * 0.5 / swing_stance_speed_ratio + swing_speed_coef = (swing_stance_speed_ratio + 1) * 0.5 + delta_left = delta_period + new_phase = self.phase + while delta_left > 0: + if 0 <= new_phase < math.pi: + delta_phase_multiplier = stance_speed_coef * _TWO_PI + new_phase += delta_left * delta_phase_multiplier + delta_left = 0 + if new_phase < math.pi: + delta_left = 0 + else: + delta_left = (new_phase - math.pi) / delta_phase_multiplier + new_phase = math.pi + else: + delta_phase_multiplier = swing_speed_coef * _TWO_PI + new_phase += delta_left * delta_phase_multiplier + if math.pi <= new_phase < _TWO_PI: + delta_left = 0 + else: + delta_left = (new_phase - _TWO_PI) / delta_phase_multiplier + new_phase = 0 + return math.fmod(new_phase, _TWO_PI) + + def progress_phase(self, delta_period, swing_stance_ratio): + """Updates the phase based on the current phase, delta period and ratio. + + Args: + delta_period: The fraction of the period to add to the current phase of + the integrator. If set to 1, the integrator will accomplish one full + period and return the same phase. The calculated phase will depend on + the current phase (if it is in first half vs second half) and swing vs + stance speed ratio. + swing_stance_ratio: The ratio of the speed of the phase when it is in + swing (second half) vs stance (first half). Set to 1.0 by default, + making it symettric, same as a classical integrator. + """ + self.phase = self.calculate_progressed_phase(delta_period, + swing_stance_ratio) + + def get_state(self): + """Returns the sin and cos of the phase as state.""" + return [(math.cos(self.phase) + 1) / 2.0, (math.sin(self.phase) + 1) / 2.0] diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/base_client.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/base_client.py new file mode 100644 index 000000000..523780124 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/base_client.py @@ -0,0 +1,63 @@ +"""Base class for simulation client.""" + +import enum +from typing import Text + +GRAY = (0.3, 0.3, 0.3, 1) + + +class ClientType(enum.Enum): + """Type of client.""" + BULLET = "pybullet" + + + +class ClientMode(enum.Enum): + """Client running mode.""" + + # Client is being initialized, object is being loaded, teleported, etc. + CONFIGURATION = 1 + + # Client is in a mode that simulate motion according to physics and control. + SIMULATION = 2 + + +class WrongClientModeError(Exception): + """Client mode does not meet expectation (e.g. load object in sim mode).""" + + +class BaseClient(object): + """Base class for simulation client.""" + + def __init__(self, client_type: Text = ""): + self._client_type = client_type + + # Default to configuration mode. + self._client_mode = ClientMode.CONFIGURATION + + @property + def client_type(self) -> ClientType: + return self._client_type + + def switch_mode(self, mode: ClientMode) -> bool: + """Switches running mode of simulation client and return if mode changed.""" + if mode not in (ClientMode.CONFIGURATION, ClientMode.SIMULATION): + raise ValueError(f"Invalid client mode {mode}.") + if mode == self._client_mode: + return False + self._client_mode = mode + return True + + @property + def client_mode(self) -> ClientMode: + """Returns current client mode.""" + return self._client_mode + + def _assert_in_configuration_mode(self, operation: Text = "this operation"): + """Raises exception if client is not in configuration mode.""" + if self._client_mode != ClientMode.CONFIGURATION: + raise WrongClientModeError( + f"Sim client is expected to be in configuration mode for " + f"{operation}.") + + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/curriculum_reset_helpers.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/curriculum_reset_helpers.py new file mode 100644 index 000000000..86fa8cfc3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/curriculum_reset_helpers.py @@ -0,0 +1,62 @@ +"""Implements dynamic locomotion gym env that changes based on iteration.""" +import gin + +import pybullet + + +# TODO(b/142642890): Make this reset to the initial world state first. +@gin.configurable +def gap_task_curriculum_update(env, + num_iter, + distance_to_gap_or_hurdle=1.5, + initial_gap_length=0.1, + max_iterations=500, + gap_delta=0.0008): + """Linearly increase the gap width wrt the iteration number. + + This is specific to BuildSingleGapWorld. + + Args: + env: An instance of a LocomotionGymEnv. + num_iter: The training iteration we are on. + distance_to_gap_or_hurdle: The distance to the gap. + initial_gap_length: The starting gap length. + max_iterations: The number of iterations up to which we will modify the + environment. + gap_delta: The amount to increase the gap width by for each increase of 1 in + the iteration. + """ + + gap_length = initial_gap_length + gap_delta * min(max_iterations, num_iter) + env.task.reset( + env, + distance_to_gap_or_hurdle=distance_to_gap_or_hurdle, + gap_or_hurdle_width=gap_length) + + +@gin.configurable +def gap_world_curriculum_update(env, + num_iter, + initial_second_block_x=8.15, + max_iterations=500, + gap_delta=0.0008): + """Update the world, linearly increasing gap width wrt iteration number. + + This is specific to SingleGapScene. + + Args: + env: An instance of a LocomotionGymEnv. + num_iter: The training iteration we are on. + initial_second_block_x: The initial x position of the second block. + max_iterations: The number of iterations up to which we will modify the + environment. + gap_delta: The amount to increase the gap width by for each increase of 1 in + the iteration. + """ + + ground = env.scene.ground_ids + pos = pybullet.getBasePositionAndOrientation(ground[-1])[0] + # Linearly increase the gap width to 0.5m by the last iteration. + next_x = initial_second_block_x + gap_delta * min(max_iterations, num_iter) + pybullet.resetBasePositionAndOrientation(ground[-1], (next_x, pos[1], pos[2]), + [0, 0, 0, 1]) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_loader.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_loader.py new file mode 100644 index 000000000..aa4afe495 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_loader.py @@ -0,0 +1,75 @@ +"""Load the locomotion gym env using the gin config files.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gin +from pybullet_envs.minitaur.envs_v2 import locomotion_gym_env +from pybullet_envs.minitaur.envs_v2 import multiagent_mobility_gym_env + +ROBOT_FIELD_IN_CONFIG = 'robot_params' +TASK_FIELD_IN_CONFIG = 'task_params' +PMTG_FIELD_IN_CONFIG = 'pmtg_params' +_PMTG_GIN_QUERY = 'pmtg_wrapper_env.PmtgWrapperEnv.' + + +@gin.configurable +def load(wrapper_classes=None, multiagent=False, **kwargs): + """load a pre-defined locomotion gym environment. + + The env specific settings should have been set in the gin files. + + Args: + wrapper_classes: A list of wrapper_classes. + multiagent: Whether to use multiagent environment. + **kwargs: Keyword arguments to be passed to the environment constructor. + + Returns: + env: The instance of the minitaur gym environment. + """ + # Gin config are not always specified this way (e.g. namescoped config). + # Only guery parameters when it is necessary. + if any( + k in (PMTG_FIELD_IN_CONFIG, TASK_FIELD_IN_CONFIG, ROBOT_FIELD_IN_CONFIG) + for k in kwargs): + with gin.unlock_config(): + if multiagent: + # Currently assume robots and tasks are identical + robot_class = gin.query_parameter( + 'multiagent_mobility_gym_env.MultiagentMobilityGymEnv.robot_classes' + )[0].selector + task = gin.query_parameter( + 'multiagent_mobility_gym_env.MultiagentMobilityGymEnv.tasks' + )[0].selector + else: + robot_class = gin.query_parameter( + 'locomotion_gym_env.LocomotionGymEnv.robot_class').selector + task = gin.query_parameter( + 'locomotion_gym_env.LocomotionGymEnv.task').selector + gin_prefix_dict = { + PMTG_FIELD_IN_CONFIG: _PMTG_GIN_QUERY, + TASK_FIELD_IN_CONFIG: task + '.', + ROBOT_FIELD_IN_CONFIG: robot_class + '.', + } + for field_name, field_values in kwargs.items(): + if field_name in gin_prefix_dict: + for var_name, value in field_values.items(): + gin.bind_parameter(gin_prefix_dict[field_name] + var_name, value) + else: + raise ValueError( + 'Environment argument type is not found in gin_prefix_dict.') + if multiagent: + env = multiagent_mobility_gym_env.MultiagentMobilityGymEnv() + else: + env = locomotion_gym_env.LocomotionGymEnv() + if wrapper_classes is not None: + # A little macro for the automatic list expansion + if not isinstance(wrapper_classes, list): + wrapper_classes = [wrapper_classes] + + # Wrap environments with user-provided wrappers + # (e.g. TrajectoryGeneratorWrapperEnv) + for wrapper_cls in wrapper_classes: + env = wrapper_cls(env) + return env diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/action_denormalize_wrapper.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/action_denormalize_wrapper.py new file mode 100644 index 000000000..edfead1ab --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/action_denormalize_wrapper.py @@ -0,0 +1,41 @@ +"""Denormalize the action from [-1, 1] to the env.action_space.""" + +import gin +import gym +import numpy as np + + +def _denomralize(env, action): + action = np.array(action) + low = np.array(env.action_space.low) + high = np.array(env.action_space.high) + return (high - low) / 2.0 * action + (high + low) / 2.0 + + +@gin.configurable +class ActionDenormalizeWrapper(object): + """An env wrapper that denormalize the action from [-1, 1] to the bounds.""" + + def __init__(self, gym_env): + """Initializes the wrapper.""" + self._gym_env = gym_env + self.action_space = gym.spaces.Box( + low=-1.0, + high=1.0, + shape=self._gym_env.action_space.low.shape, + dtype=np.float32) + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array between [-1.0, 1.0]. + + Returns: + The tuple containing the observation, the reward, and the epsiode + end indicator. + """ + return self._gym_env.step(_denomralize(self._gym_env, action)) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/alternating_legs_openloop.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/alternating_legs_openloop.py new file mode 100644 index 000000000..85a8d99c2 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/alternating_legs_openloop.py @@ -0,0 +1,125 @@ +"""A trajectory generator that return signals for alternating legs.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import google_type_annotations +from __future__ import print_function + +import math +import attr +import gin +from gym import spaces +import numpy as np +from pybullet_envs.minitaur.envs.utilities import laikago_pose_utils + +TROT_GAIT = "trot" +PACE_GAIT = "pace" +NUM_MOTORS_LAIKAGO = 12 +STD_FOR_GAUSSIAN_TRAJECTORY = 0.15 +MOTION_FREQUENCY = 1.0 +MOTION_AMPLITUDE = 0.25 +ACTION_BOUND = 0.25 + + +# TODO(b/131193449): Add a test to this class. +@gin.configurable +class LaikagoAlternatingLegsTrajectoryGenerator(object): + """A trajectory generator that return signals for alternating legs.""" + + def __init__( + self, + init_abduction=laikago_pose_utils.LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + init_hip=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + init_knee=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE, + amplitude=MOTION_AMPLITUDE, + frequency=MOTION_FREQUENCY, + gait=PACE_GAIT, # can be TROT_GAIT or PACE_GAIT + ): + """Initializes the controller.""" + self._pose = np.array( + attr.astuple( + laikago_pose_utils.LaikagoPose( + abduction_angle_0=init_abduction, + hip_angle_0=init_hip, + knee_angle_0=init_knee, + abduction_angle_1=init_abduction, + hip_angle_1=init_hip, + knee_angle_1=init_knee, + abduction_angle_2=init_abduction, + hip_angle_2=init_hip, + knee_angle_2=init_knee, + abduction_angle_3=init_abduction, + hip_angle_3=init_hip, + knee_angle_3=init_knee))) + action_high = np.array([ACTION_BOUND] * NUM_MOTORS_LAIKAGO) + self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) + self.amplitude = amplitude + self.period = 1.0 / frequency + self.gait = gait + + def _alternating_legs_trajectory(self, t): + """The reference trajectory of each joint when alternating legs. + + Args: + t: The time since the latest robot reset. + + Returns: + An array of 12 desired motor angles. + """ + phase_in_period = (t % self.period) / self.period + is_first_half_gait = phase_in_period < 0.5 + if self.gait == TROT_GAIT and is_first_half_gait: + phases = [0, 1, 1, 0] # 0 means stance and 1 means retraction. + elif self.gait == TROT_GAIT and not is_first_half_gait: + phases = [1, 0, 0, 1] + elif self.gait == PACE_GAIT and is_first_half_gait: + phases = [0, 1, 0, 1] + elif self.gait == PACE_GAIT and not is_first_half_gait: + phases = [1, 0, 1, 0] + else: + raise ValueError("{} gait is not supported in alternating legs.".format( + self.gait)) + + phase_step_center = 0.25 if is_first_half_gait else 0.75 + std = STD_FOR_GAUSSIAN_TRAJECTORY + # Uses Gaussian instead of sine for gentle foot touch down. + # The following joint angles are added to self._pose. + retract_hip_angle = self.amplitude * math.exp( + -(phase_in_period - phase_step_center) * + (phase_in_period - phase_step_center) / (std * std)) + retract_knee_angle = -2.0 * retract_hip_angle + retract_abduction_angle = 0.0 + stance_hip_angle = 0.0 + stance_knee_angle = 0.0 + stance_abduction_angle = 0.0 + angles = [] + for is_retract in phases: + if is_retract: + angles.extend([retract_abduction_angle, retract_hip_angle, + retract_knee_angle]) + else: + angles.extend([stance_abduction_angle, stance_hip_angle, + stance_knee_angle]) + return np.array(angles) + + def reset(self): + pass + + def get_action(self, current_time, input_action): + """Computes the trajectory according to input time and action. + + Args: + current_time: The time in gym env since reset. + input_action: A numpy array. The input leg pose from a NN controller. + + Returns: + A numpy array. The desired motor angles. + """ + + return self._pose + self._alternating_legs_trajectory( + current_time) + input_action + + def get_observation(self, input_observation): + """Get the trajectory generator's observation.""" + + return input_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/curriculum_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/curriculum_wrapper_env.py new file mode 100644 index 000000000..1e2f33586 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/curriculum_wrapper_env.py @@ -0,0 +1,50 @@ +"""A wrapped LocomotionGymEnv with functions that change the world and task.""" + +import gin + + +@gin.configurable +class CurriculumWrapperEnv(object): + """A wrapped LocomotionGymEnv with an evolving environment.""" + + def __init__(self, + gym_env, + num_iter=0, + curriculum_world_update=None, + curriculum_task_update=None): + """Initializes the wrapped env. + + Args: + gym_env: An instance of a (potentially previously wrapped) + LocomotionGymEnv. + num_iter: The training iteration we are on. + curriculum_world_update: A function that updates the environment based on + the iteration. Takes in the environment as an argument. + curriculum_task_update: A function that updates the task (eg. + observations) based on the iteration. Takes in the environment as an + argument. + """ + self._gym_env = gym_env + self._num_iter = num_iter + self._curriculum_world_update = curriculum_world_update + self._curriculum_task_update = curriculum_task_update + + def modify(self, step=0): + self._num_iter = step + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def reset(self, *args, **kwargs): + """Reset and adjust the environment.""" + self._gym_env.reset(*args, **kwargs) + if self._curriculum_world_update is not None: + self._curriculum_world_update(self._gym_env, self._num_iter) + if self._curriculum_task_update is not None: + self._curriculum_task_update(self._gym_env, self._num_iter) + return self._get_observation() + + # Used for testing. + @property + def num_iter(self): + return self._num_iter diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/depth_uv_to_footplacement_wrapper.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/depth_uv_to_footplacement_wrapper.py new file mode 100644 index 000000000..4b86b6df6 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/depth_uv_to_footplacement_wrapper.py @@ -0,0 +1,357 @@ +"""Change the action from uv of the depth map to xyz in world frame.""" + +import copy +import math +from typing import Sequence +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.robots import laikago_kinematic_constants +from pybullet_envs.minitaur.robots import quadruped_base +from pybullet_envs.minitaur.robots import wheeled_robot_base_sim + +UNIT_QUATERNION = (0, 0, 0, 1) +# A small gap between the target foot position and the floor. +FLOOR_HEIGHT_EPSILON = 0.02 +FOOT_HEIGHT_CLEARNCE = 0.075 +BASE_MOVEMENT_SPEED = 0.3 +INIT_ABDUCTION_ANGLE = laikago_kinematic_constants.INIT_ABDUCTION_ANGLE +INIT_HIP_ANGLE = laikago_kinematic_constants.INIT_HIP_ANGLE +INIT_KNEE_ANGLE = laikago_kinematic_constants.INIT_KNEE_ANGLE +NUM_LEGS = laikago_kinematic_constants.NUM_LEGS +COM_VIZ_BOX_SIZE = [0.025, 0.025, 0.005] +_DEFAULT_JOINT_POSE = (INIT_ABDUCTION_ANGLE, INIT_HIP_ANGLE, + INIT_KNEE_ANGLE) * NUM_LEGS +# Weights for computing the target COM from the supporting feet locations. +# The target COM for the front feet are biased forward for better robot +# stability and seeing farther in distance, which enables larger steps. +_SUPPORT_WEIGHT_MAP = [[0.0, 0.4, 0.3, 0.3], [0.4, 0.0, 0.3, 0.3], + [1.0 / 3, 1.0 / 3, 0.0, 1.0 / 3], + [1.0 / 3, 1.0 / 3, 1.0 / 3, 0.0]] +_TASK_SENSOR_NAME = "sensors" + + +@gin.configurable +class DepthUVToFootPlacementWrapper(object): + """Changes the action from a point in depth map to a point in the world frame. + + Attributes: + observation: The current observation of the environment. + last_action: The last that was used to step the environment. + env_step_counter: The number of control steps that have been elapesed since + the environment is reset. + action_space: The action space of the environment. + """ + + def __init__(self, + gym_env, + visualization=True, + foot_movement=False, + foot_clearance_height=FOOT_HEIGHT_CLEARNCE, + base_movement_speed=BASE_MOVEMENT_SPEED, + foothold_update_frequency=4): + """Initializes the wrapper. + + Args: + gym_env: the wrapped gym environment. The robot is controlled + kinematically when gym_env.robot inherits from WheeledRobotBase and is + controlled dynamically using a static gait controller when gym_env.robot + inherits from QudrupedBase. + visualization: whether to draw a sphere that represents the foothold + position. + foot_movement: whether move the toe to the desired foothold position using + IK for visualization/debugging purpose. + foot_clearance_height: the maximum height of the swing foot. + base_movement_speed: the speed of the robot base. + foothold_update_frequency: the frequency of updating the foothold, which + is the same as the frequency of the steps. The default value 4 means + four steps (1 complete cycle of static gait) per second. + """ + self._gym_env = gym_env + self._num_control_steps_per_foothold_update = max( + 1, int(1.0 / foothold_update_frequency / self._gym_env.env_time_step)) + self.last_action = None + self._visualization = visualization + self._foot_movement = foot_movement + self._foot_clearance_height = foot_clearance_height + self._base_movement_speed = base_movement_speed + self._step_counter = 0 + self.observation_space.spaces["toe_position"] = gym.spaces.Box( + np.array([-1.0] * 3), np.array([1.0] * 3)) + self.action_space = gym.spaces.Box( + np.array([-1.0] * 2), np.array([1.0] * 2)) + self.task.reset(self) + if hasattr(self.task, _TASK_SENSOR_NAME): + self.observation_space.spaces[ + self.task.get_name()] = self.task.observation_space + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def reset(self, **kwargs): + """Reset the environment.""" + obs = self._gym_env.reset(**kwargs) + self.task.reset(self) + self._step_counter = 0 + current_end_effector_pos = np.array( + self._gym_env.robot.foot_positions()[self.task.swing_foot_id]) + self.last_action = [0, 0] + if self._visualization: + self._create_foot_placement_visualization() + self._create_com_visualization() + self._initial_local_toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=False)) + + # Move COM to prepare for the first swing step. + if isinstance(self._gym_env.robot, quadruped_base.QuadrupedBase): + obs, _ = self._move_com_dynamic( + self.task.swing_foot_id, + self._num_control_steps_per_foothold_update // 3 * 2) + + # TODO(b/157614175): Adds a toe_position_sensor. + obs["toe_position"] = current_end_effector_pos + obs["vision"] = self.task.get_depth_image_for_foot() + obs["LastAction"] = self.last_action + + self._observation = obs + return self._observation + + def _move_kinematic(self): + """Move robot kinematically. + + Returns: + The tuple containing the observation and env info. + """ + if self._foot_movement: + toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=True)) + toe_positions[:, 2] = FLOOR_HEIGHT_EPSILON + destination_foothold_xyz_global = self.task.get_foothold_location( + self.last_action, use_world_frame=True) + destination_foothold_xyz_global[2] = FLOOR_HEIGHT_EPSILON + joint_pose = _DEFAULT_JOINT_POSE + for i in range(self._num_control_steps_per_foothold_update): + if self._foot_movement: + alpha = i / (self._num_control_steps_per_foothold_update - 1) + toe_positions_over_time = copy.deepcopy(toe_positions) + toe_positions_over_time[self.task.swing_foot_id] = ( + self._construct_foot_trajectories( + alpha, toe_positions[self.task.swing_foot_id], + destination_foothold_xyz_global, self._foot_clearance_height)) + joint_pose = np.array( + self.robot.motor_angles_from_foot_positions( + toe_positions_over_time, position_in_world_frame=True)[1]) + action = { + wheeled_robot_base_sim.BASE_ACTION_NAME: + (self._base_movement_speed, 0), + wheeled_robot_base_sim.BODY_ACTION_NAME: + joint_pose, + } + obs, _, _, info = self._gym_env.step(action) + return obs, info + + def _move_com_dynamic(self, swing_foot_id, num_control_steps): + """Move robot COM to the weightd center of the support polygon through dynamics. + + Args: + swing_foot_id: Index of the swing foot. + num_control_steps: Total number of control steps for moving the COM. + + Returns: + The tuple containing the observation and env info. + """ + + toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=False)) + support_polygon_center = np.array([0.0, 0.0, 0.0]) + for i in range(NUM_LEGS): + if i != swing_foot_id: + support_polygon_center += toe_positions[i] * _SUPPORT_WEIGHT_MAP[ + swing_foot_id][i] + support_polygon_center[2] = 0.0 + + for i in range(num_control_steps): + alpha = i / (num_control_steps - 1) + # Create a flat phase towards the end to stabilize the com movement. + alpha = np.clip(alpha * 1.1, 0, 1) + toe_positions_over_time = copy.deepcopy( + toe_positions) - alpha * support_polygon_center + # Use initial toe height to maintain the overal base height. + for j in range(len(toe_positions_over_time)): + toe_positions_over_time[j][2] = self._initial_local_toe_positions[j][2] + joint_pose = np.array( + self.robot.motor_angles_from_foot_positions( + toe_positions_over_time, position_in_world_frame=False)[1]) + obs, _, _, info = self._gym_env.step(joint_pose) + self._update_com_visualization() + return obs, info + + def _swing_leg_dynamic(self, swing_foot_id, destination_foothold_xyz_local, + num_control_steps): + """Move swing leg to the target foothold through IK and dynamics. + + Args: + swing_foot_id: Index of the swing foot. + destination_foothold_xyz_local: Target foothold position. + num_control_steps: Total number of control steps for swinging the leg. + + Returns: + The tuple containing the observation and env info. + """ + + local_toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=False)) + + # Move swing leg + for i in range(num_control_steps): + alpha = i / (num_control_steps - 1) + toe_positions_over_time = copy.deepcopy(local_toe_positions) + toe_positions_over_time[swing_foot_id] = ( + self._construct_foot_trajectories(alpha, + local_toe_positions[swing_foot_id], + destination_foothold_xyz_local, + self._foot_clearance_height)) + joint_pose = np.array( + self.robot.motor_angles_from_foot_positions( + toe_positions_over_time, position_in_world_frame=False)[1]) + + obs, _, _, info = self._gym_env.step(joint_pose) + return obs, info + + def _move_dynamic(self): + """Move robot dynamically. + + Returns: + The tuple containing the observation and env info. + """ + destination_foothold_xyz_local = self.task.get_foothold_location( + self.last_action, use_world_frame=False) + # Lift the target foothold slightly to account for thickness of the feet. + destination_foothold_xyz_local[2] += FLOOR_HEIGHT_EPSILON + + # Swing leg in the first 1/3 of the duration. + self._swing_leg_dynamic(self.task.swing_foot_id, + destination_foothold_xyz_local, + self._num_control_steps_per_foothold_update // 3) + + # Move COM in the rest 2/3 of the duration. + obs, info = self._move_com_dynamic( + self.task.next_swing_foot_id, + self._num_control_steps_per_foothold_update // 3 * 2) + + return obs, info + + def step(self, action: Sequence[float]): + """Steps the wrapped environment. + + Args: + action: 2 dimensional numpy array between [-1.0, 1.0]. They represents the + depth image pixel index. We assume that only one foot is swinging in + this wrapper, and this is the foothold location for that swinging leg. + The order of the swinging leg and the index of the current swinging leg + is defined in stepstone_visiontask.py. + + Returns: + The tuple containing the observation, the reward, and the episode + end indicator. + """ + self.last_action = action + reward = self.task(self) + done = self.task.done(self) + if self._visualization: + self._update_foothold_visualization(action) + + if isinstance(self._gym_env.robot, quadruped_base.QuadrupedBase): + obs, info = self._move_dynamic() + else: + obs, info = self._move_kinematic() + + self._step_counter += 1 + current_end_effector_pos = np.array( + self._gym_env.robot.foot_positions()[self.task.swing_foot_id]) + obs["toe_position"] = current_end_effector_pos + obs["vision"] = self.task.get_depth_image_for_foot() + self._observation = obs + return obs, reward, done, info + + def _construct_foot_trajectories(self, alpha, swing_foot_start_position, + swing_foot_destination, + foot_clearance_height): + """Construct the target foot position for the swing foot. + + Args: + alpha: a float in [0.0, 1.0] indicating the phase of the swing foot. + swing_foot_start_position: foot position at the beginning of the swing + motion. + swing_foot_destination: target foot position at the end of the swing + motion. + foot_clearance_height: the maximum height of the swing foot. + + Returns: + The interpolated swing foot position. + """ + new_swing_foot_position = swing_foot_start_position + alpha * ( + swing_foot_destination - swing_foot_start_position) + new_swing_foot_position[2] += ( + foot_clearance_height * math.sin((alpha) * math.pi)) + return new_swing_foot_position + + def _create_foot_placement_visualization(self): + """Creates a visualization sphere that represents the foothold position.""" + visual_id = self._gym_env.pybullet_client.createVisualShape( + self._gym_env.pybullet_client.GEOM_SPHERE, + radius=0.02, + rgbaColor=[0.7, 0.7, 0.7, 1]) + self._foothold_visual_body = self._gym_env.pybullet_client.createMultiBody( + baseMass=0, baseVisualShapeIndex=visual_id, basePosition=[0, 0, 0]) + + def _update_foothold_visualization(self, action): + """Moves the visualization sphere that represents the next foothold.""" + foothold_xyz = self.task.get_foothold_location( + action, use_world_frame=True) + self._gym_env.pybullet_client.resetBasePositionAndOrientation( + self._foothold_visual_body, foothold_xyz, UNIT_QUATERNION) + + def _create_com_visualization(self): + """Creates visualization boxes for COM and support polygon center.""" + visual_id = self._gym_env.pybullet_client.createVisualShape( + self._gym_env.pybullet_client.GEOM_BOX, + halfExtents=COM_VIZ_BOX_SIZE, + rgbaColor=[0.7, 0.5, 0.5, 1]) + self._support_polygon_center_visual_body = self._gym_env.pybullet_client.createMultiBody( + baseMass=0, baseVisualShapeIndex=visual_id, basePosition=[0, 0, 0]) + + visual_id = self._gym_env.pybullet_client.createVisualShape( + self._gym_env.pybullet_client.GEOM_BOX, + halfExtents=COM_VIZ_BOX_SIZE, + rgbaColor=[0.5, 0.75, 0.5, 1]) + self._projected_com_visual_body = self._gym_env.pybullet_client.createMultiBody( + baseMass=0, baseVisualShapeIndex=visual_id, basePosition=[0, 0, 0]) + + def _update_com_visualization(self): + """Moves the visualization for the COM and support polygon center.""" + toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=True)) + support_polygon_center_global = np.array([0.0, 0.0, 0.0]) + for i in range(NUM_LEGS): + if i != self.task.next_swing_foot_id: + support_polygon_center_global += toe_positions[i] * _SUPPORT_WEIGHT_MAP[ + self.task.next_swing_foot_id][i] + support_polygon_center_global[2] = 0.0 + self._gym_env.pybullet_client.resetBasePositionAndOrientation( + self._support_polygon_center_visual_body, support_polygon_center_global, + UNIT_QUATERNION) + + com = np.copy(self._gym_env.robot.base_position) + com[2] = 0.0 + self._gym_env.pybullet_client.resetBasePositionAndOrientation( + self._projected_com_visual_body, com, UNIT_QUATERNION) + + @property + def observation(self): + return self._observation + + @property + def env_step_counter(self): + return self._step_counter diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/fixed_steptime_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/fixed_steptime_wrapper_env.py new file mode 100644 index 000000000..c06ffacab --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/fixed_steptime_wrapper_env.py @@ -0,0 +1,92 @@ +"""A wrapper that controls the timing between steps. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import google_type_annotations +from __future__ import print_function + +import time +import gin + + +@gin.configurable +class FixedSteptimeWrapperEnv(object): + """A wrapped LocomotionGymEnv with timing control between steps.""" + + def __init__(self, + gym_env, + desired_time_between_steps=None): + """Initializes the wrapper env. + + Args: + gym_env: An instance of LocomotionGymEnv. + desired_time_between_steps: The desired time between steps in seconds. + If this is None, it is set to the env_time_step of the gym_env. + """ + self._gym_env = gym_env + if desired_time_between_steps is None: + self._desired_time_between_steps = gym_env.env_time_step + else: + self._desired_time_between_steps = desired_time_between_steps + + self._last_reset_time = time.time() + self._last_step_time = time.time() + self._step_counter = 0 + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def reset(self, initial_motor_angles=None, reset_duration=1.0): + """Reset the environment. + + This function records the timing of the reset. + + Args: + initial_motor_angles: Not used. + reset_duration: Not used. + + Returns: + The observation of the environment after reset. + """ + obs = self._gym_env.reset(initial_motor_angles=initial_motor_angles, + reset_duration=reset_duration) + self._last_reset_time = time.time() + self._last_step_time = time.time() + self._step_counter = 0 + return obs + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + """ + time_between_steps = time.time() - self._last_step_time + if time_between_steps < self._desired_time_between_steps: + time.sleep(self._desired_time_between_steps - time_between_steps) + self._last_step_time = time.time() + self._step_counter += 1 + return self._gym_env.step(action) + + @property + def elapsed_time(self): + """Returns the elapsed time in seconds.""" + return time.time() - self._last_reset_time + + @property + def steps_per_second(self): + """Returns the average number of time steps for 1 second.""" + return self._step_counter / self.elapsed_time + + @property + def seconds_per_step(self): + """Returns the average time between steps.""" + return self.elapsed_time / self._step_counter diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/ik_based_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/ik_based_wrapper_env.py new file mode 100644 index 000000000..da80bf159 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/ik_based_wrapper_env.py @@ -0,0 +1,109 @@ +"""A wrapped Quadruped with Inverse Kinematics based controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gin +import gym +import numpy as np +from pybullet_envs.minitaur.robots import vision60 +from pybullet_envs.minitaur.robots.utilities import kinematics + +ACTION_DIM_PER_LEG = 3 +ACTION_DIM_BASE = 7 +ACTION_DIM_TOTAL = vision60.NUM_LEGS * ACTION_DIM_PER_LEG + ACTION_DIM_BASE + + +@gin.configurable +class IKBasedWrapperEnv(object): + """An env using IK to convert toe positions to joint angles.""" + + def __init__(self, + gym_env, + toe_indices=(3, 7, 11, 15), + abduction_motor_ids=(0, 3, 6, 9)): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + toe_indices: A list of four pybullet joint indices for the four toes. [3, + 7, 11, 15] for the vision60. + abduction_motor_ids: A list of four pybullet joint indices for the four + abuduction motors. [0, 3, 6, 9] for the vision60. + """ + lower_bound = np.array([-1.0] * ACTION_DIM_TOTAL) + upper_bound = np.array([1.0] * ACTION_DIM_TOTAL) + self._gym_env = gym_env + self.action_space = gym.spaces.Box(lower_bound, upper_bound) + self._toe_ids = toe_indices + self._abduction_motor_ids = abduction_motor_ids + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def get_toe_indices(self): + return self._toe_ids + + def _joint_angles_from_toe_positions_and_base_pose(self, ik_actions): + """Uses Inverse Kinematics to calculate jont angles. + + Args: + ik_actions: The action should be local (x, y, z) for each toe. action for + each leg [x, y, z] in a local frame. This local frame is transformed + relative to the COM frame using a given translation, and rotation. The + total action space would be 3 + 4 + 3 * ACTION_DIM_PER_LEG = 16. + + Returns: + A list of joint angles. + """ + assert len(ik_actions) == ACTION_DIM_TOTAL + + base_translation_index = vision60.NUM_LEGS * ACTION_DIM_PER_LEG + base_rotation_index = vision60.NUM_LEGS * ACTION_DIM_PER_LEG + 3 + + base_translation = ik_actions[ + base_translation_index:base_translation_index + 3] + base_rotation = ik_actions[base_rotation_index:base_rotation_index + 4] + desired_joint_angles = [] + for i in range(vision60.NUM_LEGS): + local_toe_pos = ik_actions[i * ACTION_DIM_PER_LEG:i * ACTION_DIM_PER_LEG + + ACTION_DIM_PER_LEG] + leg_joint_ids = [ + self._abduction_motor_ids[i], self._abduction_motor_ids[i] + 1, + self._abduction_motor_ids[i] + 2 + ] + + desired_joint_angles.extend( + kinematics.joint_angles_from_link_position( + robot=self._gym_env.robot, + link_position=local_toe_pos, + link_id=self._toe_ids[i], + joint_ids=leg_joint_ids, + base_translation=base_translation, + base_rotation=base_rotation)) + + return desired_joint_angles + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the modified observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + + """ + if action is None: + raise ValueError('Action cannot be None') + + desired_joint_angles = self._joint_angles_from_toe_positions_and_base_pose( + ik_actions=action) + observation, reward, done, _ = self._gym_env.step(desired_joint_angles) + + return observation, reward, done, _ diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/imitation_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/imitation_wrapper_env.py new file mode 100644 index 000000000..bbe14ea83 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/imitation_wrapper_env.py @@ -0,0 +1,101 @@ +"""A wrapper for motion imitation environment.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gin +import gym +import numpy as np + + +@gin.configurable +class ImitationWrapperEnv(object): + """An env using for training policy with motion imitation.""" + + def __init__(self, gym_env): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + """ + self._gym_env = gym_env + self.observation_space = self._build_observation_space() + + self.seed() + return + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the modified observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + + """ + original_observation, reward, done, _ = self._gym_env.step(action) + observation = self._modify_observation(original_observation) + + return observation, reward, done, _ + + @gin.configurable('imitation_wrapper_env.ImitationWrapperEnv.reset') + def reset(self, initial_motor_angles=None, reset_duration=1.0): + """Resets the robot's position in the world or rebuild the sim world. + + The simulation world will be rebuilt if self._hard_reset is True. + + Args: + initial_motor_angles: A list of Floats. The desired joint angles after + reset. If None, the robot will use its built-in value. + reset_duration: Float. The time (in seconds) needed to rotate all motors + to the desired initial values. + + Returns: + A numpy array contains the initial observation after reset. + """ + original_observation = self._gym_env.reset(initial_motor_angles, reset_duration) + observation = self._modify_observation(original_observation) + return observation + + def _modify_observation(self, original_observation): + """Appends target observations from the reference motion to the observations. + + Args: + original_observation: A numpy array containing the original observations. + + Returns: + A numpy array contains the initial original concatenated with target + observations from the reference motion. + """ + target_observation = self._task.build_target_obs() + observation = np.concatenate([original_observation, target_observation], axis=-1) + return observation + + def _build_observation_space(self): + """Constructs the observation space, including target observations from + the reference motion. + + Returns: + Observation space representing the concatenations of the original + observations and target observations. + """ + obs_space0 = self._gym_env.observation_space + low0 = obs_space0.low + high0 = obs_space0.high + + task_low, task_high = self._task.get_target_obs_bounds() + low = np.concatenate([low0, task_low], axis=-1) + high = np.concatenate([high0, task_high], axis=-1) + + obs_space = gym.spaces.Box(low, high) + + return obs_space diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/mpc_locomotion_wrapper.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/mpc_locomotion_wrapper.py new file mode 100644 index 000000000..faebcdfea --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/mpc_locomotion_wrapper.py @@ -0,0 +1,794 @@ +"""An env that uses MPC-based motion controller to realize higher level footstep planning.""" + +import copy +import enum +from typing import Sequence + +import dataclasses +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import com_height_estimator +from pybullet_envs.minitaur.agents.baseline_controller import gait_generator as gait_generator_lib +from pybullet_envs.minitaur.agents.baseline_controller import imu_based_com_velocity_estimator +from pybullet_envs.minitaur.agents.baseline_controller import multi_state_estimator +from pybullet_envs.minitaur.agents.baseline_controller import openloop_gait_generator +from pybullet_envs.minitaur.agents.baseline_controller import torque_stance_leg_controller +from pybullet_envs.minitaur.robots import laikago_kinematic_constants + +_UNIT_QUATERNION = (0, 0, 0, 1) +_NUM_LEGS = laikago_kinematic_constants.NUM_LEGS +_MOTORS_PER_LEG = 3 +_DEFAULT_BODY_HEIGHT = 0.45 +_DEFAULT_BASE_SPEED = (0.0, 0.0) +_DEFAULT_BASE_TWIST_SPEED = 0.0 +_DEFAULT_ROLL_PITCH = (0.0, 0.0) +_DEFAULT_SWING_TARGET = (0.0, 0.0, 0.0) +_DEFAULT_SWING_CLERANCE = 0.05 +_MOTOR_KP = [220.0] * 12 +_MOTOR_KD = [0.3, 2.0, 2.0] * 4 +_BASE_VELOCITY_ACTION_RANGE = ((-1.0, -0.1), (1.0, 0.1)) +_BASE_TWIST_SPEED_ACTION_RANGE = (-0.5, 0.5) +_BASE_HEIGHT_ACTION_RANGE = (0.3, 0.5) +# Action bound for the swing target in local x, y, z direction. +_SWING_TARGET_ACTION_RANGE = ((-0.3, -0.1, -0.25), (0.3, 0.1, 0.25)) +_PITCH_ROLL_ACTION_RANGE = (-0.35, 0.35) +_SWING_CLEARANCE_ACTION_RANGE = (0.05, 0.3) +_SWING_TARGET_DELTA_ACTION_RANGE = ((-0.02, -0.01, -0.02), (0.02, 0.01, 0.02)) +_SWING_CLEARANCE_DELTA_RANGE = (-0.02, 0.02) + + +@gin.configurable +@dataclasses.dataclass +class BaseTargetHorizontalComVelocityHeuristic(object): + """A class for mapping swing foot targets to a heuristic target com velocity.""" + horizontal_com_velocity_heuristic: np.ndarray = np.zeros(2) + + def update_horizontal_com_velocity_heuristic(self, hip_relative_swing_targets, + com_velocity, swing_durations): + del hip_relative_swing_targets, com_velocity, swing_durations + pass + + def reset(self): + self.horizontal_com_velocity_heuristic = np.zeros(2) + + +# TODO(magicmelon): Add a one-pager to explain the inverse raibert heuristics. +@gin.configurable +class InverseRaibertTargetHorizontalComVelocityHeuristic( + BaseTargetHorizontalComVelocityHeuristic): + """A class for mapping swing foot targets to a target com velocity with Raibert Heuristics.""" + + def __init__(self, gains=(-0.25, -0.1)): + self._gains = np.array(gains) + + def update_horizontal_com_velocity_heuristic(self, hip_relative_swing_targets, + com_velocity, swing_durations): + assert len(hip_relative_swing_targets) == len(swing_durations) + target_com_horizontal_velocities = [] + for i in range(len(hip_relative_swing_targets)): + target_com_horizontal_velocity = ( + com_velocity / 2.0 * swing_durations[i] - + hip_relative_swing_targets[i]) / self._gains + com_velocity + target_com_horizontal_velocities.append(target_com_horizontal_velocity) + if target_com_horizontal_velocities: + self.horizontal_com_velocity_heuristic = np.mean( + target_com_horizontal_velocities, axis=0) + + +@gin.constants_from_enum +class Gait(enum.Enum): + """The possible gaits.""" + WALK = 0 + TROT = 1 + + +def _select_gait(gait_type=Gait.WALK): + """Selects a gait pattern. + + Args: + gait_type: which gait to use. + + Returns: + A tuple of (stance_duration, duty_factor, initial_phase) + """ + # Each gait is composed of stance_duration, duty_factor, and + # init_phase_full_cycle. + if gait_type == Gait.TROT: + return [0.3] * 4, [0.6] * 4, [0, 0.5, 0.5, 0] + elif gait_type == Gait.WALK: + return [0.75] * 4, [0.8] * 4, [0.25, 0.75, 0.5, 0] + else: + raise NotImplementedError + + +@gin.configurable +class MPCLocomotionWrapper(object): + """An env that uses MPC-based motion controller to realize footstep planning. + + The env takes as input the target position of the swing feet and the target + base movements, and internally uses an MPC-based controller to achieve these + targets. It assumes that the robot follows a given gait pattern, specified + during initialization. + Before each foot starts to swing, the env will request from the policy a + target swing location and height defined in the local frame w.r.t the + default toe position. During the swing of the foot, the policy can adjust + the base velocity, height, roll, pitch, and twist. Optionally, the policy + can also output a delta to the last target swing location to adjust the + swing trajectory during the swing + Observations (introduced in this env): + gait_phases (4D): Normalized phase within the gait cycle for each foot. + feet_states (4D): State of each foot. -1: stance, 1: swing, -2: lose + contact, 2: early contact. + need_new_swing_target (4D): Whether the foot needs a new swing target at + the current step. Set to 1 when the foot switches to swing from a + different state. When equals to 0, the corresponding foot target will + not have effect. + estimated_base_speed (3D): Estimated base velocity. + estimated_body_height (1D): Estimated base height. + heuristics_com_velocity (3D): Target base velocity calculated from the + input step-length using inverse Raibert heuristics. Used when + compute_heuristic_com_speed is True. + current_toe_target: Immediate tracking targets for the four feet in the + local frame. + Action components: + swing_targets (12D): Used in HL_LL and HL_only mode. Specifies + the swing target for each foot w.r.t the default local toe position. + swing_clearance (4D): Used in HL_LL and HL_only mode. Specifies + the height of the highest point in the swing trajectory. + swing_targets_delta (12D): Used if policy_output_swing_action_delta. + Specifies the change in the swing target for each foot. + swing_clearance_delta (4D): Used if policy_output_swing_action_delta. + Specifies the change in the swing clearance for each foot. + target_base_velocity (2D): Target base velocity in the horizontal plane. + Used when compute_heuristic_com_speed is False. + base_twist (1D): Target base twist. + base_height (1D): Target base height. + base_roll_pitch (2D): Target base roll and pitch. + + Attributes: + observation: The current observation of the environment. + last_action: The last that was used to step the environment. + env_step_counter: The number of control steps that have been elapesed since + the environment is reset. + action_space: The action space of the environment. + """ + + def __init__( + self, + gym_env, + swing_target_action_range=_SWING_TARGET_ACTION_RANGE, + swing_clearance_action_range=_SWING_CLEARANCE_ACTION_RANGE, + pitch_action_range=_PITCH_ROLL_ACTION_RANGE, + roll_action_range=_PITCH_ROLL_ACTION_RANGE, + base_velocity_action_range=_BASE_VELOCITY_ACTION_RANGE, + base_twist_action_range=_BASE_TWIST_SPEED_ACTION_RANGE, + base_height_action_range=_BASE_HEIGHT_ACTION_RANGE, + policy_output_swing_action_delta=False, + swing_target_delta_range=_SWING_TARGET_DELTA_ACTION_RANGE, + swing_clearance_delta_range=_SWING_CLEARANCE_DELTA_RANGE, + foot_friction_coeff=0.5, + contact_detection_force_threshold=0.0, + locomotion_gait=Gait.WALK, + target_horizontal_com_velocity_heuristic=BaseTargetHorizontalComVelocityHeuristic( + ), + robot_mass_in_mpc=235.0 / 9.8, + control_frequency=20, + com_velocity_estimator_class=imu_based_com_velocity_estimator + .IMUBasedCOMVelocityEstimator): + """Initializes the wrapper. + + Args: + gym_env: the wrapped gym environment. + swing_target_action_range: range for the swing targets specified before + each swing. + swing_clearance_action_range: range for the swing clearance. + pitch_action_range: range for the target body pitch. + roll_action_range: range for the target body roll. + base_velocity_action_range: range for the base velocity. + base_twist_action_range: range for the base twist. + base_height_action_range: range for the base height. + policy_output_swing_action_delta: whether to allow the policy to output an + adjustment to the last swing target during the swing motion. + swing_target_delta_range: range for the adjustment of the swing target. + swing_clearance_delta_range: range for the swing clearance adjustments. + foot_friction_coeff: friction on the feet. + contact_detection_force_threshold: Threshold of the contact sensor for + determining whether a foot is in contact. Use 20 for real robot and 0 + for simulation. + locomotion_gait: Gait to be used. + target_horizontal_com_velocity_heuristic: . + robot_mass_in_mpc: mass of the robot used in MPC. + control_frequency: frequency of querying the policy. The internal MPC + controller can have higher frequency. Note that the policy outputs a + swing target and clearance at each query, however, it is only used by + the environment at the beginning of each swing phase (when + need_new_swing_target is 1). + com_velocity_estimator_class: class of the com velocity estimator. Use + IMUBasedCOMVelocityEstimator for estimating velocity from IMU sensor and + contact states. Use COMVelocityEstimator for using the ground-truth com + velocity (e.g. when mocap is available). + """ + self._gym_env = gym_env + self._time_per_control_step = 1.0 / control_frequency + self._foot_friction_coeff = foot_friction_coeff + self._contact_detection_force_threshold = contact_detection_force_threshold + self._locomotion_gait = locomotion_gait + self._policy_output_swing_action_delta = policy_output_swing_action_delta + self._target_horizontal_com_velocity_heuristic = target_horizontal_com_velocity_heuristic + + self.last_action = None + + self._configure_action_space( + swing_target_action_range, swing_clearance_action_range, + pitch_action_range, roll_action_range, base_velocity_action_range, + base_twist_action_range, base_height_action_range, + policy_output_swing_action_delta, swing_target_delta_range, + swing_clearance_delta_range) + + self._configure_observation_space() + + # Set up the MPC controller + stance_duration, duty_factor, initial_phase = _select_gait(locomotion_gait) + self._gait_generator = openloop_gait_generator.OpenloopGaitGenerator( + self._gym_env.robot, stance_duration, duty_factor, initial_phase, + contact_detection_force_threshold) + self._com_velocity_estimator = com_velocity_estimator_class( + self._gym_env.robot) + self._com_height_estimator = com_height_estimator.COMHeightEstimator( + self._gym_env.robot) + self._state_estimator = multi_state_estimator.MultiStateEstimator( + self._gym_env.robot, + state_estimators=[ + self._com_velocity_estimator, self._com_height_estimator + ]) + self._stance_controller = torque_stance_leg_controller.TorqueStanceLegController( + self._gym_env.robot, + self._gait_generator, + self._state_estimator, + desired_speed=np.array(_DEFAULT_BASE_SPEED), + desired_twisting_speed=_DEFAULT_BASE_TWIST_SPEED, + desired_body_height=_DEFAULT_BODY_HEIGHT, + desired_roll_pitch=np.array(_DEFAULT_ROLL_PITCH), + body_mass=robot_mass_in_mpc) + + def _configure_observation_space(self): + """Configure the observation space.""" + self.observation_space.spaces["gait_phases"] = gym.spaces.Box( + np.array([-1.0] * 4), np.array([1.0] * 4)) + self.observation_space.spaces["feet_states"] = gym.spaces.Box( + np.array([-2.0] * 4), np.array([2.0] * 4)) + self.observation_space.spaces["need_new_swing_target"] = gym.spaces.Box( + np.array([0.0] * 4), np.array([1.0] * 4)) + self.observation_space.spaces["estimated_base_speed"] = gym.spaces.Box( + np.array([-1.0, -1.0, -1.0]), np.array([1.0, 1.0, 1.0])) + self.observation_space.spaces["estimated_body_height"] = gym.spaces.Box( + np.array([0.35]), np.array([0.5])) + self.observation_space.spaces["heuristics_com_velocity"] = gym.spaces.Box( + np.array([-1.0] * 2), np.array([1.0] * 2)) + self.observation_space.spaces["current_toe_target"] = gym.spaces.Box( + np.array([-1.0, -1.0, -1.0] * _NUM_LEGS), + np.array([1.0, 1.0, 1.0] * _NUM_LEGS)) + + # Needed so that LastActionSensor uses the correct action space. + for s in self.all_sensors(): + s.on_reset(self) + for sensor in self.all_sensors(): + if sensor.get_name() not in self._gym_config.ignored_sensor_list: + if hasattr(sensor, "observation_space"): + self.observation_space.spaces[ + sensor.get_name()] = sensor.observation_space + + self.task.reset(self) + if hasattr(self.task, "observation_space"): + self.observation_space.spaces[ + self.task.get_name()] = self.task.observation_space + + def _configure_action_space(self, swing_target_action_range, + swing_clearance_action_range, pitch_action_range, + roll_action_range, base_velocity_action_range, + base_twist_action_range, base_height_action_range, + policy_output_swing_action_delta, + swing_target_delta_range, + swing_clearance_delta_range): + """Configure the action space.""" + ac_lb = np.array([]) + ac_ub = np.array([]) + + # Index of different part of the actions within the action array. + self._action_start_indices = {} + self._action_dimensions = {} + self._action_names = [] + + # Swing targets and swing clearance. + for leg_id in range(_NUM_LEGS): + self._action_start_indices["swing_targets_" + str(leg_id)] = len(ac_lb) + self._action_dimensions["swing_targets_" + str(leg_id)] = len( + swing_target_action_range[0]) + self._action_names.append("swing_targets_" + str(leg_id)) + ac_lb = np.concatenate([ac_lb, swing_target_action_range[0]]) + ac_ub = np.concatenate([ac_ub, swing_target_action_range[1]]) + for leg_id in range(_NUM_LEGS): + self._action_start_indices["swing_clearance_" + str(leg_id)] = len(ac_lb) + self._action_dimensions["swing_clearance_" + str(leg_id)] = 1 + self._action_names.append("swing_clearance_" + str(leg_id)) + ac_lb = np.concatenate([ac_lb, [swing_clearance_action_range[0]]]) + ac_ub = np.concatenate([ac_ub, [swing_clearance_action_range[1]]]) + + # Delta to the swing targets and clearance. + if policy_output_swing_action_delta: + for leg_id in range(_NUM_LEGS): + self._action_start_indices["swing_targets_delta_" + + str(leg_id)] = len(ac_lb) + self._action_dimensions["swing_targets_delta_" + str(leg_id)] = len( + swing_target_delta_range[0]) + self._action_names.append("swing_targets_delta_" + str(leg_id)) + ac_lb = np.concatenate([ac_lb, swing_target_delta_range[0]]) + ac_ub = np.concatenate([ac_ub, swing_target_delta_range[1]]) + for leg_id in range(_NUM_LEGS): + self._action_start_indices["swing_clearance_delta_" + + str(leg_id)] = len(ac_lb) + self._action_dimensions["swing_clearance_delta_" + str(leg_id)] = 1 + self._action_names.append("swing_clearance_delta_" + str(leg_id)) + ac_lb = np.concatenate([ac_lb, [swing_clearance_delta_range[0]]]) + ac_ub = np.concatenate([ac_ub, [swing_clearance_delta_range[1]]]) + + # Desired CoM velocity actions + # Do not include the action if bounds are all equal to zero + if not np.all(np.array(base_velocity_action_range) == 0): + self._action_start_indices["target_base_velocity"] = len(ac_lb) + self._action_dimensions["target_base_velocity"] = 2 + self._action_names.append("target_base_velocity") + ac_lb = np.concatenate([ + ac_lb, + [base_velocity_action_range[0][0], base_velocity_action_range[0][1]] + ]) + ac_ub = np.concatenate([ + ac_ub, + [base_velocity_action_range[1][0], base_velocity_action_range[1][1]] + ]) + + # Base twist speed action + self._action_start_indices["base_twist"] = len(ac_lb) + self._action_dimensions["base_twist"] = 1 + self._action_names.append("base_twist") + ac_lb = np.concatenate([ac_lb, [base_twist_action_range[0]]]) + ac_ub = np.concatenate([ac_ub, [base_twist_action_range[1]]]) + + # Base height action + self._action_start_indices["base_height"] = len(ac_lb) + self._action_dimensions["base_height"] = 1 + self._action_names.append("base_height") + ac_lb = np.concatenate([ac_lb, [base_height_action_range[0]]]) + ac_ub = np.concatenate([ac_ub, [base_height_action_range[1]]]) + + # Roll-pitch action + self._action_start_indices["base_roll_pitch"] = len(ac_lb) + self._action_dimensions["base_roll_pitch"] = 2 + self._action_names.append("base_roll_pitch") + ac_lb = np.concatenate( + [ac_lb, [roll_action_range[0], pitch_action_range[0]]]) + ac_ub = np.concatenate( + [ac_ub, [roll_action_range[1], pitch_action_range[1]]]) + + self.action_space = gym.spaces.Box(ac_lb, ac_ub) + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def _fill_observations(self, obs): + """Fill the additional observations from this wrapper.""" + phase_offset = np.array([ + 0 if leg_state == gait_generator_lib.LegState.STANCE else 1 + for leg_state in self._gait_generator.desired_leg_state + ]) + obs["gait_phases"] = self._gait_generator.normalized_phase - phase_offset + obs["feet_states"] = [] + for leg_state in self._gait_generator.desired_leg_state: + if leg_state == gait_generator_lib.LegState.STANCE: + obs["feet_states"].append(-1) + if leg_state == gait_generator_lib.LegState.SWING: + obs["feet_states"].append(1) + if leg_state == gait_generator_lib.LegState.EARLY_CONTACT: + obs["feet_states"].append(2) + if leg_state == gait_generator_lib.LegState.LOSE_CONTACT: + obs["feet_states"].append(-2) + obs["need_new_swing_target"] = np.copy(self._need_new_swing_target) + obs["estimated_base_speed"] = self._state_estimator.com_velocity_body_yaw_aligned_frame + obs["estimated_body_height"] = [self._state_estimator.estimated_com_height] + obs["heuristics_com_velocity"] = np.copy( + self._target_horizontal_com_velocity_heuristic + .horizontal_com_velocity_heuristic) + obs["current_toe_target"] = np.copy(self._current_toe_target) + + def _reset_mpc_controller(self): + """Reset the state of the MPC controller.""" + self._gait_generator.reset(0.0) + self._state_estimator.reset(0.0) + self._stance_controller.reset(0.0) + self._stance_controller.desired_speed = np.array(_DEFAULT_BASE_SPEED) + self._stance_controller.desired_twisting_speed = _DEFAULT_BASE_TWIST_SPEED + self._stance_controller.desired_body_height = _DEFAULT_BODY_HEIGHT + self._stance_controller.desired_roll_pitch = np.array(_DEFAULT_ROLL_PITCH) + self._mpc_reset_time = self.robot.GetTimeSinceReset() + + def reset(self, **kwargs): + """Reset the environment.""" + self._gym_env.reset(**kwargs) + + self._reset_mpc_controller() + + self._last_leg_state = copy.copy(self._gait_generator.leg_state) + + self._initial_local_toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=False)) + + self._current_toe_target = np.reshape( + copy.deepcopy(self._initial_local_toe_positions), 3 * _NUM_LEGS) + + # Record last lift off position for each foot for computing swing + # trajectory. + self._lift_off_positions = copy.deepcopy(self._initial_local_toe_positions) + + # Swing command by policy at the beginning of each swing phase. + self._nominal_swing_leg_commands = [] + for _ in range(_NUM_LEGS): + self._nominal_swing_leg_commands.append({ + "swing_target": np.array(_DEFAULT_SWING_TARGET), + "swing_clearance": _DEFAULT_SWING_CLERANCE + }) + # Actual swing leg commands that incorporated potential delta adjustments + # from the policy. + self._actual_swing_leg_commands = [] + for _ in range(_NUM_LEGS): + self._actual_swing_leg_commands.append({ + "swing_target": np.array(_DEFAULT_SWING_TARGET), + "swing_clearance": _DEFAULT_SWING_CLERANCE + }) + + self._need_new_swing_target = np.array([0.0] * _NUM_LEGS) + + self._target_horizontal_com_velocity_heuristic.reset() + + self.last_action = [] + for leg_id in range(_NUM_LEGS): + self.last_action.extend( + [0.0] * self._action_dimensions["swing_targets_" + str(leg_id)]) + for leg_id in range(_NUM_LEGS): + self.last_action.extend( + [0.0] * self._action_dimensions["swing_clearance_" + str(leg_id)]) + if self._policy_output_swing_action_delta: + for leg_id in range(_NUM_LEGS): + self.last_action.extend( + [0.0] * + self._action_dimensions["swing_targets_delta_" + str(leg_id)]) + for leg_id in range(_NUM_LEGS): + self.last_action.extend( + [0.0] * + self._action_dimensions["swing_clearance_delta_" + str(leg_id)]) + print("=========", len(self.last_action)) + if "target_base_velocity" in self._action_names: + self.last_action.extend([0.0] * + self._action_dimensions["target_base_velocity"]) + self.last_action.extend([0.0] * self._action_dimensions["base_twist"]) + self.last_action.extend([0.0] * self._action_dimensions["base_height"]) + self.last_action.extend([0.0] * self._action_dimensions["base_roll_pitch"]) + + # Needed for LastActionSensor to use the correct last_action. + for s in self.all_sensors(): + s.on_reset(self) + obs = self._get_observation() + self._fill_observations(obs) + + self.task.reset(self) + self._step_counter = 0 + + # Change feet friction. + for link_id in list( + self.robot.urdf_loader.get_end_effector_id_dict().values()): + self.pybullet_client.changeDynamics( + self.robot.robot_id, + link_id, + lateralFriction=self._foot_friction_coeff) + self._observation = obs + + return self._observation + + def _clean_up_action(self, action): + """Return a cleaned up action to use previous value or zero for components not used. + + Args: + action: Input action from the policy. + + Returns: + A cleaned up action where components not used in this step is replaced + with zero or value from previous steps. + """ + cleaned_action = np.copy(action) + for leg_id in range(_NUM_LEGS): + if not self._need_new_swing_target[leg_id]: + swing_target_name = "swing_targets_" + str(leg_id) + swing_targets_start_index = self._action_start_indices[ + swing_target_name] + swing_targets_end_index = swing_targets_start_index + self._action_dimensions[ + swing_target_name] + cleaned_action[swing_targets_start_index: + swing_targets_end_index] = self.last_action[ + swing_targets_start_index:swing_targets_end_index] + + swing_clearance_name = "swing_clearance_" + str(leg_id) + swing_clearance_start_index = self._action_start_indices[ + swing_clearance_name] + cleaned_action[swing_clearance_start_index] = self.last_action[ + swing_clearance_start_index] + if self._policy_output_swing_action_delta: + for leg_id in range(_NUM_LEGS): + if self._observation["feet_states"][leg_id] != 1: + swing_target_delta_name = "swing_targets_delta_" + str(leg_id) + swing_targets_delta_start_index = self._action_start_indices[ + swing_target_delta_name] + swing_targets_delta_end_index = swing_targets_delta_start_index + self._action_dimensions[ + swing_target_delta_name] + cleaned_action[ + swing_targets_delta_start_index: + swing_targets_delta_end_index] = self.last_action[ + swing_targets_delta_start_index:swing_targets_delta_end_index] + + swing_clearance_delta_name = "swing_clearance_delta_" + str(leg_id) + swing_clearance_delta_start_index = self._action_start_indices[ + swing_clearance_delta_name] + cleaned_action[swing_clearance_delta_start_index] = self.last_action[ + swing_clearance_delta_start_index] + + return cleaned_action + + def _get_toe_tracking_target(self, lift_off_position, phase, swing_clearance, + landing_position): + """Get the tracking target for the toes during the swing phase. + + The swing toe will move 70% of the distance in the first half of the swing. + Intuitely, we want to move the swing foot quickly to the target landing + location and stay above the ground, in this way the control is more robust + to perturbations to the body that may cause the swing foot to drop onto + the ground earlier than expected. This is a common practice similar + to the MIT cheetah and Marc Raibert's original controllers. After the + designated swing motion finishes, we also command the foot to go down + for a short ditance (0.03m). This is to mitigate issues when the swing + finishes before it touches the ground. + + Args: + lift_off_position: Local position when the foot leaves ground. + phase: Normalized phase of the foot in the current swing cycle. + swing_clearance: Height of the highest point in the swing trajectory. + landing_position: Target landing position in the local space. + + Returns: + The interpolated foot target for the current step. + """ + # Up vector in the world coordinate (without considering yaw). + rotated_up_vec = np.array( + self.pybullet_client.multiplyTransforms( + (0, 0, 0), + self.pybullet_client.getQuaternionFromEuler( + (self.robot.base_roll_pitch_yaw[0], + self.robot.base_roll_pitch_yaw[1], 0)), (0, 0, 1), + _UNIT_QUATERNION)[0]) + + # Linearly interpolate the trajectory to get the foot target. + keyframe_timings = [0.0, 0.45, 0.9, 0.9001, 1.0] + peak_toe_position = 0.3 * lift_off_position + 0.7 * landing_position + rotated_up_vec * swing_clearance + # TODO(magicmelon): Update the gait generator to warp the gait and keep + # the swing leg going down until it touches the ground. + landing_position_pressing_down = landing_position - rotated_up_vec * 0.03 + keyframe_positions = np.array([ + lift_off_position, peak_toe_position, landing_position, + landing_position_pressing_down, landing_position_pressing_down + ]) + + target_toe_positions = np.array([ + np.interp(phase, keyframe_timings, keyframe_positions[:, 0]), + np.interp(phase, keyframe_timings, keyframe_positions[:, 1]), + np.interp(phase, keyframe_timings, keyframe_positions[:, 2]) + ]) + return target_toe_positions + + def step(self, action: Sequence[float]): + """Steps the wrapped environment. + + Args: + action: + + Returns: + The tuple containing the observation, the reward, and the episode + end indicator. + """ + self.last_action = self._clean_up_action(action) + + obs, reward, done, info = self._step_motion_controller(self.last_action) + self._step_counter += 1 + self._fill_observations(obs) + self._observation = obs + + return obs, reward, done, info + + def _extract_action(self, action, name): + return action[self. + _action_start_indices[name]:self._action_start_indices[name] + + self._action_dimensions[name]] + + def _get_swing_foot_ids(self): + swing_foot_ids = [] + for leg_id in range(_NUM_LEGS): + if self._gait_generator.leg_state[ + leg_id] == gait_generator_lib.LegState.SWING: + swing_foot_ids.append(leg_id) + return swing_foot_ids + + def _update_gait_states_and_flags(self): + """Update gait-related variables and flags.""" + current_leg_state = self._gait_generator.leg_state + for leg_id in range(_NUM_LEGS): + if current_leg_state[ + leg_id] == gait_generator_lib.LegState.SWING and self._last_leg_state[ + leg_id] != gait_generator_lib.LegState.SWING: + self._lift_off_positions[leg_id] = self._gym_env.robot.foot_positions( + )[leg_id] + self._need_new_swing_target[leg_id] = 1 + self._last_leg_state = copy.copy(current_leg_state) + + com_estimate_leg_indices = [] + for leg_id in range(_NUM_LEGS): + # Use the ones not swinging to estimate the com height + if leg_id not in self._get_swing_foot_ids(): + com_estimate_leg_indices.append(leg_id) + self._com_height_estimator.com_estimate_leg_indices = com_estimate_leg_indices + self._state_estimator.update(self.robot.GetTimeSinceReset() - + self._mpc_reset_time) + self._gait_generator.update(self.robot.GetTimeSinceReset() - + self._mpc_reset_time) + + def _process_action(self, action): + """Process the action and set relevant variables.""" + # Extract the swing targets from the input action. + for leg_id in range(_NUM_LEGS): + if self._need_new_swing_target[leg_id]: + self._nominal_swing_leg_commands[leg_id]["swing_target"] = ( + self._extract_action(action, "swing_targets_" + str(leg_id))) + self._nominal_swing_leg_commands[leg_id]["swing_clearance"] = ( + self._extract_action(action, "swing_clearance_" + str(leg_id))) + + self._actual_swing_leg_commands[leg_id]["swing_target"] = np.copy( + self._nominal_swing_leg_commands[leg_id]["swing_target"]) + self._actual_swing_leg_commands[leg_id][ + "swing_clearance"] = self._nominal_swing_leg_commands[leg_id][ + "swing_clearance"] + + # Reset the flags so the next high level commands are not used until the + # next swing happens. + self._need_new_swing_target = np.zeros(_NUM_LEGS) + + # Extract the delta swing targets from the input action. + if self._policy_output_swing_action_delta: + for leg_id in self._get_swing_foot_ids(): + self._actual_swing_leg_commands[leg_id]["swing_target"] = ( + self._nominal_swing_leg_commands[leg_id]["swing_target"] + + self._extract_action(action, "swing_targets_delta_" + str(leg_id))) + self._actual_swing_leg_commands[leg_id]["swing_clearance"] = ( + self._nominal_swing_leg_commands[leg_id]["swing_clearance"] + + self._extract_action(action, + "swing_clearance_delta_" + str(leg_id))) + # Extract the target base movement commands from the input action. + if "target_base_velocity" in self._action_names: + self._target_base_velocity_from_policy = self._extract_action( + action, "target_base_velocity") + else: + self._target_base_velocity_from_policy = np.zeros(3) + desired_twist_speed = self._extract_action(action, "base_twist") + desired_body_height = self._extract_action(action, "base_height") + desired_roll_pitch = self._extract_action(action, "base_roll_pitch") + + self._stance_controller.desired_twisting_speed = desired_twist_speed + self._stance_controller.desired_body_height = desired_body_height + self._stance_controller.desired_roll_pitch = desired_roll_pitch + + def _compute_swing_action(self): + """Compute actions for the swing legs.""" + local_toe_positions = np.array( + self._gym_env.robot.foot_positions(position_in_world_frame=False)) + toe_positions_over_time = copy.deepcopy(local_toe_positions) + + for leg_id in self._get_swing_foot_ids(): + target_toe_positions_local = self._get_toe_tracking_target( + self._lift_off_positions[leg_id], + self._gait_generator.normalized_phase[leg_id], + self._actual_swing_leg_commands[leg_id]["swing_clearance"], + self._actual_swing_leg_commands[leg_id]["swing_target"] + + self._initial_local_toe_positions[leg_id]) + toe_positions_over_time[leg_id] = target_toe_positions_local + self._current_toe_target = np.reshape( + copy.deepcopy(toe_positions_over_time), 3 * _NUM_LEGS) + return np.array( + self.robot.motor_angles_from_foot_positions( + toe_positions_over_time, position_in_world_frame=False)[1]) + + def _compute_stance_action(self): + """Compute actions for the stance legs.""" + + # update target com velocity by combining policy output and heuristics + hip_relative_swing_targets = [] + swing_durations = [] + com_horizontal_velocity = np.array( + self._state_estimator.com_velocity_body_yaw_aligned_frame)[0:2] + for swing_id in self._get_swing_foot_ids(): + hip_relative_swing_targets.append( + np.array([ + self._actual_swing_leg_commands[swing_id]["swing_target"][0], + self._actual_swing_leg_commands[swing_id]["swing_target"][1] + ])) + swing_durations.append(self._gait_generator.swing_duration[swing_id]) + + self._target_horizontal_com_velocity_heuristic.update_horizontal_com_velocity_heuristic( + hip_relative_swing_targets, com_horizontal_velocity, swing_durations) + self._stance_controller.desired_speed = np.array( + self._target_base_velocity_from_policy) + self._stance_controller.desired_speed[ + 0: + 2] += self._target_horizontal_com_velocity_heuristic.horizontal_com_velocity_heuristic + + # compute actions for the stance leg + return self._stance_controller.get_action() + + def _combine_swing_stance_action(self, swing_action, stance_action): + """Combine stance and swing leg actions.""" + feet_contact_states = copy.copy(self._gait_generator.leg_state) + actions = [] + for leg_id in range(_NUM_LEGS): + if leg_id in self._get_swing_foot_ids( + ) and feet_contact_states[leg_id] == gait_generator_lib.LegState.SWING: + for motor_id_in_leg in range(_MOTORS_PER_LEG): + actions.extend( + (swing_action[leg_id * _MOTORS_PER_LEG + motor_id_in_leg], + _MOTOR_KP[leg_id * _MOTORS_PER_LEG + motor_id_in_leg], 0.0, + _MOTOR_KD[leg_id * _MOTORS_PER_LEG + motor_id_in_leg], 0.0)) + else: + for motor_id_in_leg in range(_MOTORS_PER_LEG): + actions.extend(stance_action[leg_id * _MOTORS_PER_LEG + + motor_id_in_leg]) + return actions + + def _step_motion_controller(self, action): + """Run the MPC controller to advance the robot's state.""" + + self._process_action(action) + + # run MPC-based control for a certain amount of time + total_reward = 0.0 + start_time = self._gym_env.robot.GetTimeSinceReset() + while self._gym_env.robot.GetTimeSinceReset( + ) - start_time < self._time_per_control_step: + self._update_gait_states_and_flags() + + swing_action = self._compute_swing_action() + + stance_action = self._compute_stance_action() + + actions = self._combine_swing_stance_action(swing_action, stance_action) + + obs, rew, done, info = self._gym_env.step(actions) + if self._stance_controller.qp_solver_fail: + done = True + total_reward += rew + + if done: + return obs, 0.0, done, info + + return obs, total_reward, done, info + + @property + def observation(self): + return self._observation + + @property + def env_step_counter(self): + return self._step_counter diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/observation_dictionary_to_array_wrapper.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/observation_dictionary_to_array_wrapper.py new file mode 100644 index 000000000..34e6049cc --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/observation_dictionary_to_array_wrapper.py @@ -0,0 +1,65 @@ +"""An env wrapper that flattens the observation dictionary to an array.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gym +import gin +from pybullet_envs.minitaur.envs_v2.utilities import env_utils + + +@gin.configurable +class ObservationDictionaryToArrayWrapper(gym.Env): + """An env wrapper that flattens the observation dictionary to an array.""" + + def __init__(self, gym_env, observation_excluded=()): + """Initializes the wrapper.""" + self.observation_excluded = observation_excluded + self._gym_env = gym_env + self.observation_space = self._flatten_observation_spaces( + self._gym_env.observation_space) + self.action_space = self._gym_env.action_space + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def _flatten_observation_spaces(self, observation_spaces): + flat_observation_space = env_utils.flatten_observation_spaces( + observation_spaces=observation_spaces, + observation_excluded=self.observation_excluded) + return flat_observation_space + + def _flatten_observation(self, input_observation): + """Flatten the dictionary to an array.""" + return env_utils.flatten_observations( + observation_dict=input_observation, + observation_excluded=self.observation_excluded) + + def seed(self, seed=None): + return self._gym_env.seed(seed) + + def reset(self, initial_motor_angles=None, reset_duration=1.0): + observation = self._gym_env.reset( + initial_motor_angles=initial_motor_angles, + reset_duration=reset_duration) + return self._flatten_observation(observation) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the flattened observation, the reward, the epsiode + end indicator. + """ + observation_dict, reward, done, _ = self._gym_env.step(action) + return self._flatten_observation(observation_dict), reward, done, _ + + def render(self, mode='human'): + return self._gym_env.render(mode) + + def close(self): + return self._gym_env.close() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_inplace_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_inplace_wrapper_env.py new file mode 100644 index 000000000..0a274d399 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_inplace_wrapper_env.py @@ -0,0 +1,176 @@ +"""A wrapped MinitaurGymEnv with a built-in controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import attr +from gym import spaces +import numpy as np +import gin +from pybullet_envs.minitaur.agents.trajectory_generator import tg_inplace +from pybullet_envs.minitaur.envs.utilities import laikago_pose_utils +from pybullet_envs.minitaur.envs.utilities import minitaur_pose_utils + +_NUM_LEGS = 4 +_LAIKAGO_NUM_ACTIONS = 12 +_FREQ_LOWER_BOUND = 0.0 +_FREQ_UPPER_BOUND = 3 +_DEFAULT_AMPLITUDE_STANCE = -0.02 +_DEFAULT_AMPLITUDE_LIFT = 0.9 +_DEFAULT_CENTER_EXTENSION = 0 +_DEFAULT_STANCE_LIFT_CUTOFF = 2 * np.pi * 0.67 +_DEFAULT_RESIDUAL_RANGE = 0.4 +_LAIKAGO_KNEE_ACTION_INDEXES = [2, 5, 8, 11] +MINITAUR_INIT_EXTENSION_POS = 2.0 +MINITAUR_INIT_SWING_POS = 0.0 + + +@gin.configurable +class PmtgInplaceWrapperEnv(object): + """A wrapped LocomotionGymEnv with a built-in trajectory generator.""" + + def __init__(self, + gym_env, + freq_lower_bound=_FREQ_LOWER_BOUND, + freq_upper_bound=_FREQ_UPPER_BOUND, + residual_range=_DEFAULT_RESIDUAL_RANGE, + amplitude_stance=_DEFAULT_AMPLITUDE_STANCE, + amplitude_lift=_DEFAULT_AMPLITUDE_LIFT, + center_extension=_DEFAULT_CENTER_EXTENSION, + stance_lift_cutoff=_DEFAULT_STANCE_LIFT_CUTOFF): + """Initializes the TG inplace wrapper class. + + Args: + gym_env: the gym environment to wrap on. + freq_lower_bound: minimum frequency that the TGs can be propagated at. + freq_upper_bound: maximum frequency that the TGs can be propagated at. + residual_range: range of residuals that can be added to tg outputs. + amplitude_stance: stance amplitude of TG (see tg_inplace.py for details). + amplitude_lift: swing amplitude of TG (see tg_inplace.py for details). + center_extension: center extension of TG (see tg_inplace.py for details). + stance_lift_cutoff: phase cutoff between stance and lift phase (see + tg_inplace.py for details). + """ + self._gym_env = gym_env + self._num_actions = gym_env.robot.num_motors + self._tg_phases = tg_inplace.reset() + self._tg_params = dict( + amplitude_stance=amplitude_stance, + amplitude_lift=amplitude_lift, + center_extension=center_extension, + stance_lift_cutoff=stance_lift_cutoff) + + # Add the action boundaries for delta time, one per integrator. + action_low = np.hstack( + ([-residual_range] * self._num_actions, [freq_lower_bound] * _NUM_LEGS)) + action_high = np.hstack( + ([residual_range] * self._num_actions, [freq_upper_bound] * _NUM_LEGS)) + self.action_space = spaces.Box(action_low, action_high) + + # Set the observation space and boundaries. + if hasattr(self._gym_env.observation_space, "spaces"): + self.observation_space = self._gym_env.observation_space + self.observation_space.spaces["pmtg_inplace"] = spaces.Box( + -1 * np.ones(2 * _NUM_LEGS), np.ones(2 * _NUM_LEGS)) + else: + lower_bound = self._gym_env.observation_space.low + upper_bound = self._gym_env.observation_space.high + lower_bound = np.hstack((lower_bound, [-1.] * 2 * _NUM_LEGS)) + upper_bound = np.hstack((upper_bound, [1.] * 2 * _NUM_LEGS)) + self.observation_space = spaces.Box(lower_bound, upper_bound) + + def __getattr__(self, attrb): + return getattr(self._gym_env, attrb) + + def _modify_observation(self, observation): + if isinstance(observation, dict): + observation["tg_inplace"] = np.hstack( + (np.cos(self._tg_phases), np.sin(self._tg_phases))) + return observation + else: + return np.hstack( + (observation, np.cos(self._tg_phases), np.sin(self._tg_phases))) + + def reset(self, initial_motor_angles=None, reset_duration=1.0): + """Resets the environment as well as trajectory generators.""" + self._last_real_time = 0 + self._num_step = 0 + self._tg_phases = tg_inplace.reset() + if self._num_actions == _LAIKAGO_NUM_ACTIONS: + # Use laikago's init pose as zero action. + init_pose = np.array( + attr.astuple( + laikago_pose_utils.LaikagoPose( + abduction_angle_0=laikago_pose_utils + .LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + hip_angle_0=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + knee_angle_0=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE, + abduction_angle_1=laikago_pose_utils + .LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + hip_angle_1=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + knee_angle_1=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE, + abduction_angle_2=laikago_pose_utils + .LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + hip_angle_2=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + knee_angle_2=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE, + abduction_angle_3=laikago_pose_utils + .LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + hip_angle_3=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + knee_angle_3=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE))) + self._init_pose = init_pose + observation = self._gym_env.reset(init_pose, reset_duration) + else: + # Use minitaur's init pose as zero action. + init_pose = np.array( + attr.astuple( + minitaur_pose_utils.MinitaurPose( + swing_angle_0=MINITAUR_INIT_SWING_POS, + swing_angle_1=MINITAUR_INIT_SWING_POS, + swing_angle_2=MINITAUR_INIT_SWING_POS, + swing_angle_3=MINITAUR_INIT_SWING_POS, + extension_angle_0=MINITAUR_INIT_EXTENSION_POS, + extension_angle_1=MINITAUR_INIT_EXTENSION_POS, + extension_angle_2=MINITAUR_INIT_EXTENSION_POS, + extension_angle_3=MINITAUR_INIT_EXTENSION_POS))) + initial_motor_angles = minitaur_pose_utils.leg_pose_to_motor_angles( + init_pose) + observation = self._gym_env.reset(initial_motor_angles, reset_duration) + return self._modify_observation(observation) + + def step(self, action): + """Steps the wrapped PMTG inplace environment.""" + time = self._gym_env.get_time_since_reset() + + # Convert the policy's residual actions to motor space. + if self._num_actions == _LAIKAGO_NUM_ACTIONS: + action_residual = np.array( + attr.astuple( + laikago_pose_utils.LaikagoPose(*(action[0:self._num_actions])))) + else: + action_residual = minitaur_pose_utils.leg_pose_to_motor_angles( + action[0:self._num_actions]) + + self._last_real_time = time + self._tg_phases, tg_extensions = tg_inplace.step( + self._tg_phases, action[-_NUM_LEGS:], self._gym_env.env_time_step, + self._tg_params) + # Convert TG's actions to motor space depending on the robot type. + if self._num_actions == _LAIKAGO_NUM_ACTIONS: + # If the legs have 3 DOF, apply extension directly to knee joints + action_tg_motor_space = np.zeros(self._num_actions) + for tg_idx, knee_idx in zip( + range(_NUM_LEGS), _LAIKAGO_KNEE_ACTION_INDEXES): + action_tg_motor_space[knee_idx] = tg_extensions[tg_idx] + else: + # Conversion to motor space for minitaur robot. + action_tg_motor_space = [] + for idx in range(_NUM_LEGS): + extend = tg_extensions[idx] + action_tg_motor_space.extend( + minitaur_pose_utils.swing_extend_to_motor_angles(idx, 0, extend)) + new_action = action_tg_motor_space + action_residual + if self._num_actions == _LAIKAGO_NUM_ACTIONS: + new_action += self._init_pose + original_observation, reward, done, _ = self._gym_env.step(new_action) + + return self._modify_observation(original_observation), reward, done, _ diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_wrapper_env.py new file mode 100644 index 000000000..1df14a296 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_wrapper_env.py @@ -0,0 +1,290 @@ +"""A wrapped MinitaurGymEnv with a built-in controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from gym import spaces +import numpy as np +import gin +from pybullet_envs.minitaur.agents.trajectory_generator import tg_simple +from pybullet_envs.minitaur.envs_v2.utilities import robot_pose_utils +from pybullet_envs.minitaur.robots.utilities import action_filter + +_DELTA_TIME_LOWER_BOUND = 0.0 +_DELTA_TIME_UPPER_BOUND = 3 + +_GAIT_PHASE_MAP = { + "walk": [0, 0.25, 0.5, 0.75], + "trot": [0, 0.5, 0.5, 0], + "bound": [0, 0.5, 0, 0.5], + "pace": [0, 0, 0.5, 0.5], + "pronk": [0, 0, 0, 0] +} + + +@gin.configurable +class PmtgWrapperEnv(object): + """A wrapped LocomotionGymEnv with a built-in trajectory generator.""" + + def __init__(self, + gym_env, + intensity_upper_bound=1.5, + min_delta_time=_DELTA_TIME_LOWER_BOUND, + max_delta_time=_DELTA_TIME_UPPER_BOUND, + integrator_coupling_mode="all coupled", + walk_height_coupling_mode="all coupled", + variable_swing_stance_ratio=True, + swing_stance_ratio=1.0, + residual_range=0.15, + init_leg_phase_offsets=None, + init_gait=None, + default_walk_height=0, + action_filter_enable=True, + action_filter_order=1, + action_filter_low_cut=0, + action_filter_high_cut=3.0, + action_filter_initialize=False, + leg_pose_class=None): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + intensity_upper_bound: The upper bound for intensity of the trajectory + generator. It can be used to limit the leg movement. + min_delta_time: Lower limit for the time in seconds that the trajectory + generator can be moved forward at each simulation step. The effective + frequency of the gait is based on the delta time multiplied by the + internal frequency of the trajectory generator. + max_delta_time: Upper limit for the time in seconds that the trajectory + generator can be moved forward at each simulation step. + integrator_coupling_mode: How the legs should be coupled for integrators. + walk_height_coupling_mode: The same coupling mode used for walking walking + heights for the legs. + variable_swing_stance_ratio: A boolean to indicate if the swing stance + ratio can change per time step or not. + swing_stance_ratio: Time taken by swing phase vs stance phase. This is + only relevant if variable_swing_stance_ratio is False. + residual_range: The upper limit for the residual actions that adds to the + leg motion. It is 0.15 by default, can be increased for more flexibility + or decreased to only to use the trajectory generator's motion. + init_leg_phase_offsets: The initial phases of the legs. A list of 4 + variables within [0,1). The order is front-left, rear-left, front-right + and rear-right. + init_gait: The initial gait that sets the starting phase difference + between the legs. Overrides the arg init_phase_offsets. Has to be + "walk", "trot", "bound" or "pronk". Used in vizier search. + default_walk_height: Offset for the extension of the legs for the robot. + Applied to the legs at every time step. Implicitly affects the walking + and standing height of the policy. Zero by default. Units is in + extension space (can be considered in radiant since it is a linear + transformation to motor angles based on the robot's geometry). + action_filter_enable: Use a butterworth filter for the output of the PMTG + actions (before conversion to leg swing-extend model). It forces + smoother behaviors depending on the parameters used. + action_filter_order: The order for the action_filter (1 by default). + action_filter_low_cut: The cut for the lower frequencies (0 by default). + action_filter_high_cut: The cut for the higher frequencies (3 by default). + action_filter_initialize: If the action filter should be initialized when + the first action is taken. If enabled, the filter does not affect action + value the first time it is called and fills the history with that value. + leg_pose_class: A class providing a convert_leg_pose_to_motor_angle + instance method or None. If None, robot_pose_utils is used. + + Raises: + ValueError if the controller does not implement get_action and + get_observation. + + """ + self._gym_env = gym_env + self._num_actions = gym_env.robot.num_motors + self._residual_range = residual_range + self._min_delta_time = min_delta_time + self._max_delta_time = max_delta_time + self._leg_pose_util = leg_pose_class() if leg_pose_class else None + # If not specified, default leg phase offsets to walking. + if init_gait: + if init_gait in _GAIT_PHASE_MAP: + init_leg_phase_offsets = _GAIT_PHASE_MAP[init_gait] + else: + raise ValueError("init_gait is not one of the defined gaits.") + else: + init_leg_phase_offsets = init_leg_phase_offsets or [0, 0.25, 0.5, 0.75] + # Create the Trajectory Generator based on the parameters. + self._trajectory_generator = tg_simple.TgSimple( + intensity_upper_bound=intensity_upper_bound, + integrator_coupling_mode=integrator_coupling_mode, + walk_height_coupling_mode=walk_height_coupling_mode, + variable_swing_stance_ratio=variable_swing_stance_ratio, + swing_stance_ratio=swing_stance_ratio, + init_leg_phase_offsets=init_leg_phase_offsets) + + action_dim = self._extend_action_space() + self._extend_obs_space() + + self._default_walk_height = default_walk_height + self._action_filter_enable = action_filter_enable + if self._action_filter_enable: + self._action_filter_initialize = action_filter_initialize + self._action_filter_order = action_filter_order + self._action_filter_low_cut = action_filter_low_cut + self._action_filter_high_cut = action_filter_high_cut + self._action_filter = self._build_action_filter(action_dim) + + def _extend_obs_space(self): + """Extend observation space to include pmtg phase variables.""" + # Set the observation space and boundaries. + lower_bound, upper_bound = self._get_observation_bounds() + if hasattr(self._gym_env.observation_space, "spaces"): + new_obs_space = spaces.Box(np.array(lower_bound), np.array(upper_bound)) + self.observation_space.spaces.update({"pmtg_phase": new_obs_space}) + else: + lower_bound = np.append(self._gym_env.observation_space.low, lower_bound) + upper_bound = np.append(self._gym_env.observation_space.high, upper_bound) + self.observation_space = spaces.Box( + np.array(lower_bound), np.array(upper_bound), dtype=np.float32) + + def _extend_action_space(self): + """Extend the action space to include pmtg parameters.""" + # Add the action boundaries for delta time, one per integrator. + action_low = [-self._residual_range] * self._num_actions + action_high = [self._residual_range] * self._num_actions + action_low = np.append(action_low, [self._min_delta_time] * + self._trajectory_generator.num_integrators) + action_high = np.append(action_high, [self._max_delta_time] * + self._trajectory_generator.num_integrators) + + # Add the action boundaries for parameters of the trajectory generator. + l_bound, u_bound = self._trajectory_generator.get_parameter_bounds() + action_low = np.append(action_low, l_bound) + action_high = np.append(action_high, u_bound) + self.action_space = spaces.Box( + np.array(action_low), np.array(action_high), dtype=np.float32) + return len(action_high) + + def __getattr__(self, attrb): + return getattr(self._gym_env, attrb) + + def _modify_observation(self, observation): + if isinstance(observation, dict): + observation["pmtg_phase"] = self._trajectory_generator.get_state() + return observation + else: + return np.append(observation, self._trajectory_generator.get_state()) + + def reset(self, initial_motor_angles=None, reset_duration=1.0): + """Resets the environment as well as the trajectory generator(s). + + Args: + initial_motor_angles: Unused for PMTG. Instead, it sets the legs to a pose + with the neutral action for the trajectory generator. + reset_duration: Float. The time (in seconds) needed to rotate all motors + to the desired initial values. + + Returns: + A numpy array contains the initial observation after reset. + """ + del initial_motor_angles + if self._action_filter_enable: + self._reset_action_filter() + self._last_real_time = 0 + self._num_step = 0 + self._target_speed_coef = 0.0 + if self._trajectory_generator: + self._trajectory_generator.reset() + if self._leg_pose_util: + initial_motor_angles = self._leg_pose_util.convert_leg_pose_to_motor_angles( + [0, 0, 0] * 4) + else: + initial_motor_angles = robot_pose_utils.convert_leg_pose_to_motor_angles( + self._gym_env.robot_class, [0, 0, 0] * 4) + observation = self._gym_env.reset(initial_motor_angles, reset_duration) + return self._modify_observation(observation) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the modified observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + + """ + + if action is None: + raise ValueError("Action cannot be None") + + if self._action_filter_enable: + action = self._filter_action(action) + + time = self._gym_env.get_time_since_reset() + + action_residual = action[0:self._num_actions] + # Add the default walking height offset to extension. + dimensions = len(action_residual) // 4 + action_residual[dimensions - 1::dimensions] += self._default_walk_height + # Calculate trajectory generator's output based on the rest of the actions. + delta_real_time = time - self._last_real_time + self._last_real_time = time + action_tg = self._trajectory_generator.get_actions( + delta_real_time, action[self._num_actions:]) + # If the residuals have a larger dimension, extend trajectory generator's + # actions to include abduction motors. + if len(action_tg) == 8 and len(action_residual) == 12: + for i in [0, 3, 6, 9]: + action_tg.insert(i, 0) + # Add TG actions with residual actions (in swing - extend space). + action_total = [a + b for a, b in zip(action_tg, action_residual)] + # Convert them to motor space based on the robot-specific conversions. + if self._leg_pose_util: + action_motor_space = self._leg_pose_util.convert_leg_pose_to_motor_angles( + action_total) + else: + action_motor_space = robot_pose_utils.convert_leg_pose_to_motor_angles( + self._gym_env.robot_class, action_total) + original_observation, reward, done, _ = self._gym_env.step( + action_motor_space) + + return self._modify_observation(original_observation), np.float32( + reward), done, _ + + def _get_observation_bounds(self): + """Get the bounds of the observation added from the trajectory generator. + + Returns: + lower_bounds: Lower bounds for observations. + upper_bounds: Upper bounds for observations. + """ + lower_bounds = self._trajectory_generator.get_state_lower_bounds() + upper_bounds = self._trajectory_generator.get_state_upper_bounds() + return lower_bounds, upper_bounds + + def _build_action_filter(self, num_joints): + order = self._action_filter_order + low_cut = self._action_filter_low_cut + high_cut = self._action_filter_high_cut + sampling_rate = 1 / (0.01) + a_filter = action_filter.ActionFilterButter([low_cut], [high_cut], + sampling_rate, order, + num_joints) + return a_filter + + def _reset_action_filter(self): + self._action_filter.reset() + self._action_filter_empty = True + return + + def _filter_action(self, action): + if self._action_filter_empty and self._action_filter_initialize: + # If initialize is selected and it is the first time filter is called, + # fill the buffer with that action so that it starts from that value + # instead of zero(s). + init_action = np.array(action).reshape(len(action), 1) + self._action_filter.reset(init_action) + self._action_filter_empty = False + filtered_action = self._action_filter.filter(np.array(action)) + return filtered_action diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/simple_openloop.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/simple_openloop.py new file mode 100644 index 000000000..65cb6446f --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/simple_openloop.py @@ -0,0 +1,127 @@ +"""Simple openloop trajectory generators.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import attr +from gym import spaces +import numpy as np + +import gin +from pybullet_envs.minitaur.envs_v2.utilities import laikago_pose_utils +from pybullet_envs.minitaur.envs_v2.utilities import minitaur_pose_utils + + +@gin.configurable +class MinitaurPoseOffsetGenerator(object): + """A trajectory generator that return a constant leg pose.""" + + def __init__(self, + init_swing=0, + init_extension=2.0, + init_pose=None, + action_scale=1.0, + action_limit=0.5): + """Initializes the controller. + + Args: + init_swing: the swing of the default pose offset + init_extension: the extension of the default pose offset + init_pose: the default pose offset, which is None by default. If not None, + it will define the default pose offset while ignoring init_swing and + init_extension. + action_scale: changes the magnitudes of actions + action_limit: clips actions + """ + if init_pose is None: + self._pose = np.array( + attr.astuple( + minitaur_pose_utils.MinitaurPose( + swing_angle_0=init_swing, + swing_angle_1=init_swing, + swing_angle_2=init_swing, + swing_angle_3=init_swing, + extension_angle_0=init_extension, + extension_angle_1=init_extension, + extension_angle_2=init_extension, + extension_angle_3=init_extension))) + else: # Ignore init_swing and init_extension + self._pose = np.array(init_pose) + action_high = np.array([action_limit] * minitaur_pose_utils.NUM_MOTORS) + self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) + self._action_scale = action_scale + + def reset(self): + pass + + def get_action(self, current_time=None, input_action=None): + """Computes the trajectory according to input time and action. + + Args: + current_time: The time in gym env since reset. + input_action: A numpy array. The input leg pose from a NN controller. + + Returns: + A numpy array. The desired motor angles. + """ + del current_time + return minitaur_pose_utils.leg_pose_to_motor_angles( + self._pose + self._action_scale * np.array(input_action)) + + def get_observation(self, input_observation): + """Get the trajectory generator's observation.""" + + return input_observation + + +@gin.configurable +class LaikagoPoseOffsetGenerator(object): + """A trajectory generator that return constant motor angles.""" + + def __init__( + self, + init_abduction=laikago_pose_utils.LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + init_hip=laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + init_knee=laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE, + action_limit=0.5, + ): + """Initializes the controller.""" + self._pose = np.array( + attr.astuple( + laikago_pose_utils.LaikagoPose( + abduction_angle_0=init_abduction, + hip_angle_0=init_hip, + knee_angle_0=init_knee, + abduction_angle_1=init_abduction, + hip_angle_1=init_hip, + knee_angle_1=init_knee, + abduction_angle_2=init_abduction, + hip_angle_2=init_hip, + knee_angle_2=init_knee, + abduction_angle_3=init_abduction, + hip_angle_3=init_hip, + knee_angle_3=init_knee))) + action_high = np.array([action_limit] * 12) + self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) + + def reset(self): + pass + + def get_action(self, current_time=None, input_action=None): + """Computes the trajectory according to input time and action. + + Args: + current_time: The time in gym env since reset. + input_action: A numpy array. The input leg pose from a NN controller. + + Returns: + A numpy array. The desired motor angles. + """ + del current_time + return self._pose + input_action + + def get_observation(self, input_observation): + """Get the trajectory generator's observation.""" + + return input_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/state_machine_based_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/state_machine_based_wrapper_env.py new file mode 100644 index 000000000..56a92dcd0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/state_machine_based_wrapper_env.py @@ -0,0 +1,321 @@ +"""A wrapped Quadruped with State-machine based controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import enum +import gin +import gym +import numpy as np + +NUM_LEGS = 4 +ACTION_DIM_COM = 2 +ACTION_DIM_TOE = 1 +ACTION_DIM_TOTAL = ACTION_DIM_COM + ACTION_DIM_TOE +OBSERVATION_DIM_LEG_ID = NUM_LEGS +OBSERVATION_DIM_TOE_POS = 2 * NUM_LEGS +OBSERVATION_DIM_TOTAL = OBSERVATION_DIM_TOE_POS + OBSERVATION_DIM_LEG_ID + + +# States of the state machine. +class GaitStateMachine(enum.IntEnum): + """The state machine for quadruped gait.""" + STEP_LEFT_FRONT_TOE = 0 + STEP_RIGHT_HIND_TOE = 1 + STEP_RIGHT_FRONT_TOE = 2 + STEP_LEFT_HIND_TOE = 3 + TOTAL_GAIT_STATE_NUM = 4 + + +@gin.configurable +class StateMachineBasedWrapperEnv(object): + """An env using IK to convert toe positions to joint angles. + + The state machine consists of 4 states. During each state, the center of + mass of the base link is moved first, then one of the legs will take a + step by following a planned elliptical trajectory. The legs will move in + the order of front left -> hind right -> front right -> hind left. + The state transition is determined by elapsed time since last transition. + Observation (16 dimensions): + [one hot vector of the state id, local toe positions in x and y direction] + Action (3 dimensions): + [target moving distance of the current moving leg in x direction, + target moving distance of base COM in x direction, + target moving distance of base COM in y direction] + """ + + def __init__(self, + gym_env, + default_local_toe_positions, + toe_link_indices=(3, 7, 11, 15), + foot_lift_height=0.15, + state_duration=2.0, + action_lower_bound=(-0.0, -0.25, -0.25), + action_upper_bound=(0.3, 0.25, 0.25), + state_to_foot_id=(0, 3, 2, 1)): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + default_local_toe_positions: A list of vectors that contains the default + local position of each toe. + toe_link_indices: A list of indices to the toe link. Used for calculating + local toe positions. + foot_lift_height: Specifies how high the foot lifts during swing stage. + state_duration: Specifies the duration of each state. + action_lower_bound: Lower bound for the actions. + action_upper_bound: Upper bound for the actions. + state_to_foot_id: Mapping from state machine state to foot id. + """ + self._gym_env = gym_env + + assert len(action_lower_bound) == ACTION_DIM_TOTAL + assert len(action_upper_bound) == ACTION_DIM_TOTAL + self.action_space = gym.spaces.Box( + np.array(action_lower_bound), np.array(action_upper_bound)) + observation_lower_bound = np.array([-1.0] * OBSERVATION_DIM_TOTAL) + observation_upper_bound = np.array([1.0] * OBSERVATION_DIM_TOTAL) + self.observation_space = gym.spaces.Box(observation_lower_bound, + observation_upper_bound) + self._default_local_toe_positions = default_local_toe_positions + self._toe_link_indices = toe_link_indices + self._foot_lift_height = foot_lift_height + self._state_to_foot_id = state_to_foot_id + + # Use the largest value for bounding the toe movement + self._toe_move_bound = np.max( + np.abs(np.concatenate([action_lower_bound, action_upper_bound]))) + + # Duration of each state + self.state_machine_state_duration = [state_duration] * int( + GaitStateMachine.TOTAL_GAIT_STATE_NUM) + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def _state_machine_observation(self): + """Get the current observation: [state_id, local_toe_positions].""" + observation = [] + # One-hot vector for the current state machine state. + one_hot_foot_id = np.zeros(GaitStateMachine.TOTAL_GAIT_STATE_NUM) + one_hot_foot_id[self.current_state_machine_state] = 1 + observation.extend(one_hot_foot_id) + + # Toe positions in X and Y direction in the local space. + for toe_index in range(NUM_LEGS): + toe_pos_local = self.current_local_toe_positions[toe_index] + toe_pos_local_xy = [toe_pos_local[0], toe_pos_local[1]] + observation.extend(toe_pos_local_xy) + + return observation + + def _get_constant_accel_interpolation(self, interp_ratio): + """Modify an interpolation between 0 and 1 to have constant acceleration.""" + assert interp_ratio <= 1.0 and interp_ratio >= 0.0 + if interp_ratio < 0.5: + return 0.5 * (2 * interp_ratio)**2 + else: + return -0.5 * (2 * interp_ratio - 2)**2 + 1 + + def _move_com(self, target_com_movement, time_since_transition, + state_duration): + """Get the ik action for moving the COM. + + Args: + target_com_movement: Target COM movement relative to the previous COM + position in the x-y plane. + time_since_transition: Time elapsed since last state machien transition. + state_duration: Duration of the state machine. The first half will be used + for moving COM and the second half for moving the swing leg. + + Returns: + ik_action: ik targets for moving the COM. + """ + com_movement_ratio = np.clip( + time_since_transition / ((state_duration - 0.0) / 2.0), 0, 1) + com_movement_ratio = self._get_constant_accel_interpolation( + com_movement_ratio) + current_com_movement = np.array(target_com_movement) * com_movement_ratio + + ik_action = [] + for toe_index in range(NUM_LEGS): + toe_pos_local = np.copy(self.current_local_toe_positions[toe_index]) + toe_pos_local[2] = self._default_local_toe_positions[toe_index][2] + toe_pos_local[0] -= current_com_movement[0] + toe_pos_local[1] -= current_com_movement[1] + toe_pos_local[0] = np.clip( + toe_pos_local[0], self._default_local_toe_positions[toe_index][0] - + self._toe_move_bound, + self._default_local_toe_positions[toe_index][0] + + self._toe_move_bound) + toe_pos_local[1] = np.clip( + toe_pos_local[1], self._default_local_toe_positions[toe_index][1] - + self._toe_move_bound, + self._default_local_toe_positions[toe_index][1] + + self._toe_move_bound) + ik_action.extend(toe_pos_local) + + zero_translation = [0, 0, 0] + identity_rotation = [0, 0, 0, 1] + ik_action.extend(zero_translation) + ik_action.extend(identity_rotation) + + return ik_action + + def _move_leg(self, target_toe_movement, time_since_transition, + state_duration): + """Get the ik action for moving the swing leg. + + Args: + target_toe_movement: Target toe movement relative to the default toe + position in the positive x direction. + time_since_transition: Time elapsed since last state machien transition. + state_duration: Duration of the state machine. The first half will be used + for moving COM and the second half for moving the swing leg. + + Returns: + ik_action: ik targets for moving the swing leg. + """ + + # The target toe position at the end of the movement. + target_toe_local_position = np.array(self._default_local_toe_positions[ + self._state_to_foot_id[self.current_state_machine_state]]) + target_toe_local_position[0] += target_toe_movement + + # Toe position at the beginning of the movement. + initial_toe_local_position = np.array(self.current_local_toe_positions[ + self._state_to_foot_id[self.current_state_machine_state]]) + + # Auxiliary variables for computing the interpolation between the current + # toe position and the target toe position. + toe_circle_radius = 0.5 * np.linalg.norm( + np.array(target_toe_local_position) - initial_toe_local_position) + toe_moving_direction = np.array( + target_toe_local_position) - initial_toe_local_position + toe_moving_direction /= np.max([np.linalg.norm(toe_moving_direction), 1e-5]) + toe_traj_scale_ratio = self._foot_lift_height / np.max( + [toe_circle_radius, 1e-5]) + + # Current percentage of time into the state machine. + toe_movement_ratio = np.clip( + (time_since_transition - state_duration / 2.0) / + ((state_duration - 0.0) / 2.0), 0, 1) + toe_movement_ratio = self._get_constant_accel_interpolation( + toe_movement_ratio) + toe_circle_moved_angle = np.pi * toe_movement_ratio + + # Target horizontal movement from the previous local toe position. + target_toe_horizontal_movement = toe_circle_radius - np.cos( + toe_circle_moved_angle) * toe_circle_radius + current_toe_target = np.array([ + target_toe_horizontal_movement * toe_moving_direction[0], + target_toe_horizontal_movement * toe_moving_direction[1], + np.sin(toe_circle_moved_angle) * toe_circle_radius * + toe_traj_scale_ratio + ]) + initial_toe_local_position + + ik_action = [] + for toe_index in range(NUM_LEGS): + toe_pos_local = np.copy(self.current_local_toe_positions[toe_index]) + toe_pos_local[2] = self._default_local_toe_positions[toe_index][2] + if toe_index == self._state_to_foot_id[self.current_state_machine_state]: + toe_pos_local[0] = current_toe_target[0] + toe_pos_local[1] = current_toe_target[1] + toe_pos_local[2] = current_toe_target[2] + + ik_action.extend(toe_pos_local) + + zero_translation = [0, 0, 0] + identity_rotation = [0, 0, 0, 1] + ik_action.extend(zero_translation) + ik_action.extend(identity_rotation) + return ik_action + + def _update_state_machine_transition(self): + """Update the state machine state if the duration has been reached.""" + self.current_state_machine_state = (self.current_state_machine_state + 1 + ) % GaitStateMachine.TOTAL_GAIT_STATE_NUM + self.last_state_transition_time = self.robot.GetTimeSinceReset() + + def _update_local_toe_positions(self): + """Update the local position of the toes.""" + identity_orientation = [0, 0, 0, 1] + base_position = self.robot.GetBasePosition() + base_orientation = self.robot.GetBaseOrientation() + inv_base_position, inv_base_orientation = self.pybullet_client.invertTransform( + base_position, base_orientation) + for toe_index in range(NUM_LEGS): + toe_pose_world = self.pybullet_client.getLinkState( + self.robot.quadruped, self._toe_link_indices[toe_index])[0] + toe_pos_local, _ = self.pybullet_client.multiplyTransforms( + inv_base_position, inv_base_orientation, toe_pose_world, + identity_orientation) + self.current_local_toe_positions[toe_index] = np.array(toe_pos_local) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the modified observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + + """ + if action is None: + raise ValueError("Action cannot be None") + action = np.clip(action, self.action_space.low, self.action_space.high) + sum_reward = 0 + step_num = 0 + state_duration = self.state_machine_state_duration[ + self.current_state_machine_state] + time_since_transition = self.robot.GetTimeSinceReset( + ) - self.last_state_transition_time + + # Move COM + while time_since_transition < state_duration / 2.0: + ik_actions = self._move_com(action[1:3], time_since_transition, + state_duration) + _, reward, done, _ = self._gym_env.step(ik_actions) + sum_reward += reward + step_num += 1 + time_since_transition = self.robot.GetTimeSinceReset( + ) - self.last_state_transition_time + if done: + break + self._update_local_toe_positions() + # Move Leg + while time_since_transition < state_duration: + ik_actions = self._move_leg(action[0], time_since_transition, + state_duration) + _, reward, done, _ = self._gym_env.step(ik_actions) + sum_reward += reward + step_num += 1 + time_since_transition = self.robot.GetTimeSinceReset( + ) - self.last_state_transition_time + if done: + break + self._update_local_toe_positions() + self._update_state_machine_transition() + + state_machine_observation = self._state_machine_observation() + + return state_machine_observation, sum_reward, done, _ + + def reset(self): + """Reset the simulation and state machine states.""" + self.current_state_machine_state = GaitStateMachine.STEP_LEFT_FRONT_TOE + self.last_state_transition_time = 0 + self.current_local_toe_positions = np.copy( + self._default_local_toe_positions) + + self._gym_env.reset() + + state_machine_observation = self._state_machine_observation() + + return state_machine_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/step_based_curriculum_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/step_based_curriculum_wrapper_env.py new file mode 100644 index 000000000..e6296cd80 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/step_based_curriculum_wrapper_env.py @@ -0,0 +1,231 @@ +"""A wrapped LocomotionGymEnv with functions that change the world and task.""" + +import numbers +from typing import Optional, Sequence, Text, Tuple, Union + +import numpy as np + +import gin + + +@gin.configurable +class CurriculumParameter(object): + """Definition of a env parameter tuned by a curriculum.""" + + def __init__(self, + name: Text, + init_val: numbers.Real, + bounds: Tuple[numbers.Real, numbers.Real]): + """A parameter to tune throughout the curriculum. + + Args: + name: The name of the curriculum parameter. This must be the name of + attribute of the scene class. + init_val: The value to use at the start of the curriculum. + bounds: A tuple of [lower_bound, upper_bound] defining the minimum and + maximum values of the parameter. + """ + self.name = name + + if not isinstance(bounds[0], type(bounds[1])): + raise ValueError("All elements in [bounds] must be of the same type.") + + if (init_val < min(bounds)) or (init_val > max(bounds)): + raise ValueError("Initial parameter value must lie in range defined" + " by [bounds].") + + if not isinstance(init_val, type(bounds[0])): + raise ValueError("[init_val] type must match the type of the elements" + " in [bounds].") + + self.init_val = init_val + self.bounds = bounds + self.dtype = type(init_val) + + +@gin.configurable +class RandomSamplingCurriculumParameter(CurriculumParameter): + """Env parameter whose value is sampled randomly without curriculum.""" + + def __init__(self, name: Text, bounds: Tuple[numbers.Real, numbers.Real]): + """A parameter whose value to sample randomly. + + Args: + name: The name of the parameter. + bounds: A tuple of [lower_bound, upper_bound] defining the minimum and + maximum values of the parameter. + """ + super(RandomSamplingCurriculumParameter, self).__init__( + name=name, init_val=bounds[0], bounds=bounds) + + def sample( + self, step: numbers.Real, + curriculum_steps: Optional[numbers.Real] = None) -> Union[int, float]: + del step, curriculum_steps + sampled_val = np.random.uniform(*self.bounds) + + if not isinstance(self.bounds[0], float): + sampled_val = int(round(sampled_val)) + + return sampled_val + + def __call__( + self, step: numbers.Real, + curriculum_steps: Optional[numbers.Real] = None) -> Union[int, float]: + return self.sample(step, curriculum_steps) + + +@gin.configurable +class LinearStepBasedCurriculumParameter(CurriculumParameter): + """Definition of a env parameter tuned by a linear time-based curriculum.""" + + def __init__(self, + name: Text, + init_val: numbers.Real, + bounds: Tuple[numbers.Real, numbers.Real], + curriculum_steps: Optional[numbers.Real] = None): + """A parameter to tune throughout the curriculum. + + Args: + name: The name of the curriculum parameter. This must be the name of + attribute of the scene class. + init_val: The value to use at the start of the curriculum. + bounds: A tuple of [lower_bound, upper_bound] defining the minimum and + maximum values of the parameter. + curriculum_steps: Integer defining the number of steps to take when + varying the curriculum parameter value from the init_val to either + bound. If None is specified, then the curriculum must provide the + curriculum_steps when sampling. + """ + super(LinearStepBasedCurriculumParameter, self).__init__( + name=name, init_val=init_val, bounds=bounds) + self.curriculum_steps = curriculum_steps + + def get_bounds_at_step( + self, step: numbers.Real, + curriculum_steps: Optional[numbers.Real] = None + ) -> Tuple[Union[int, float], Union[int, float]]: + """Compute the bounds of the parameter at the current step. + + Args: + step: An integer defining the current timestep. + curriculum_steps: Optional curriculum steps. Must be passed if not passed + during initialization. + Returns: + A tuple containing the lower bound and upper bound at the current step. + """ + + if not self.curriculum_steps and not curriculum_steps: + raise ValueError("curriculum_steps not defined. Must be passed upon" + " initialization or must specified by curriculum" + " wrapper env on method call.") + + if self.curriculum_steps: + curriculum_steps = self.curriculum_steps + + prog = min(float(step) / curriculum_steps, 1.0) + curr_lower_bound = self.init_val - prog * (self.init_val - self.bounds[0]) + curr_upper_bound = self.init_val + prog * (self.bounds[1] - self.init_val) + + if not isinstance(self.bounds[0], float): + curr_lower_bound = int(round(curr_lower_bound)) + curr_upper_bound = int(round(curr_upper_bound)) + + return (curr_lower_bound, curr_upper_bound) + + def sample( + self, step: numbers.Real, + curriculum_steps: Optional[numbers.Real] = None) -> Union[int, float]: + sampled_val = np.random.uniform(*self.get_bounds_at_step( + step, curriculum_steps)) + + if not isinstance(self.bounds[0], float): + sampled_val = int(round(sampled_val)) + + return sampled_val + + def __call__( + self, step: numbers.Real, + curriculum_steps: Optional[numbers.Real] = None) -> Union[int, float]: + return self.sample(step, curriculum_steps) + + +@gin.configurable +class Task(object): + """Defines a single task in the environment and its corresponding params.""" + + def __init__(self, name: Text, + curriculum_parameters: Sequence[CurriculumParameter]): + """Initialize the task. + + Args: + name: The name of the task. + curriculum_parameters: A list of CurriculumParameter instances which + define the parameters that this task makes use of. + """ + self.name = name + self.curriculum_parameters = curriculum_parameters + + +@gin.configurable +class StepBasedCurriculumWrapperEnv(object): + """A wrapper to tune the scene parameters linearly with the steps taken.""" + + def __init__(self, env, tasks: Sequence[Task], + default_curriculum_steps: Optional[numbers.Real] = None, + reset_total_step_count_val: numbers.Real = -1, + steps_before_curriculum_start: numbers.Real = 0): + """Initializes the linear curriculum wrapper env. + + Args: + env: An instance of a (potentially previously wrapped) LocomotionGymEnv. + tasks: Various tasks to shuffle through throughout the curriculum. + default_curriculum_steps: Optional default value for curriculum steps. + reset_total_step_count_val: Step at which to reset the total step count. + The internal total_step_count is reset to 0 once this value is reached. + steps_before_curriculum_start: Steps to take in environment before the + curriculum begins. + """ + self._gym_env = env + self._tasks = tasks + self._default_curriculum_steps = default_curriculum_steps + self._reset_total_step_count_val = reset_total_step_count_val + self._steps_before_curriculum_start = steps_before_curriculum_start + + # Total number of environment steps. + self._total_step_count = 0 + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def set_scene_params(self): + # Choose a task at random. + curr_task = np.random.choice(self._tasks) + + # Cycle through the task's parameters and set them in the scene. + self._gym_env.scene.reset_scene_params() + self._gym_env.scene.scene_type = curr_task.name + for curriculum_parameter in curr_task.curriculum_parameters: + setattr( + self._gym_env.scene, curriculum_parameter.name, + curriculum_parameter( + self._total_step_count - self._steps_before_curriculum_start, + self._default_curriculum_steps)) + + def reset(self, *args, **kwargs): + """Reset and adjust the environment.""" + + # Update the total step count. + self._total_step_count += self._gym_env.env_step_counter + if self._reset_total_step_count_val >= 0: + if self._total_step_count >= self._reset_total_step_count_val: + self._total_step_count = 0 + + if self._total_step_count < self._steps_before_curriculum_start: + return self._get_observation() + + self.set_scene_params() + self._gym_env.reset(*args, **kwargs) + + return self._get_observation() + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/trajectory_generator_wrapper_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/trajectory_generator_wrapper_env.py new file mode 100644 index 000000000..a2c6f0bf6 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/trajectory_generator_wrapper_env.py @@ -0,0 +1,81 @@ +"""A wrapped MinitaurGymEnv with a built-in controller.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import gin + + +@gin.configurable +class TrajectoryGeneratorWrapperEnv(object): + """A wrapped LocomotionGymEnv with a built-in trajectory generator.""" + + def __init__(self, gym_env, trajectory_generator): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + trajectory_generator: A trajectory_generator that can potentially modify + the action and observation. Typticall generators includes the PMTG and + openloop signals. Expected to have get_action and get_observation + interfaces. + + Raises: + ValueError if the controller does not implement get_action and + get_observation. + + """ + self._gym_env = gym_env + if not hasattr(trajectory_generator, 'get_action') or not hasattr( + trajectory_generator, 'get_observation'): + raise ValueError( + 'The controller does not have the necessary interface(s) implemented.' + ) + + self._trajectory_generator = trajectory_generator + + # The trajectory generator can subsume the action/observation space. + if hasattr(trajectory_generator, 'observation_space'): + self.observation_space = self._trajectory_generator.observation_space + + if hasattr(trajectory_generator, 'action_space'): + self.action_space = self._trajectory_generator.action_space + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def _modify_observation(self, observation): + return self._trajectory_generator.get_observation(observation) + + def reset(self, initial_motor_angles=None, reset_duration=1.0): + if getattr(self._trajectory_generator, 'reset'): + self._trajectory_generator.reset() + observation = self._gym_env.reset(initial_motor_angles, reset_duration) + return self._modify_observation(observation) + + def step(self, action): + """Steps the wrapped environment. + + Args: + action: Numpy array. The input action from an NN agent. + + Returns: + The tuple containing the modified observation, the reward, the epsiode end + indicator. + + Raises: + ValueError if input action is None. + + """ + + if action is None: + raise ValueError('Action cannot be None') + + new_action = self._trajectory_generator.get_action( + self._gym_env.robot.GetTimeSinceReset(), action) + + original_observation, reward, done, _ = self._gym_env.step(new_action) + + return self._modify_observation(original_observation), reward, done, _ diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/walking_wrapper.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/walking_wrapper.py new file mode 100644 index 000000000..6235a8536 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/walking_wrapper.py @@ -0,0 +1,134 @@ +"""Wraps a quadruped walking controller for navigation control.""" + +from typing import Any +import gin +from gym import spaces +import numpy as np + +from pybullet_envs.minitaur.agents.baseline_controller import com_velocity_estimator +from pybullet_envs.minitaur.agents.baseline_controller import locomotion_controller +from pybullet_envs.minitaur.agents.baseline_controller import openloop_gait_generator +from pybullet_envs.minitaur.agents.baseline_controller import raibert_swing_leg_controller +from pybullet_envs.minitaur.agents.baseline_controller import torque_stance_leg_controller + +_N_LEGS = 4 +_STANCE_DURATION_SECONDS = [ + 0.25 +] * _N_LEGS # The stance phase duration for each leg. +_DUTY_FACTOR = [ + 0.6 +] * _N_LEGS # Percentage of the leg in the stance phase within the cycle. +_BODY_HEIGHT = 0.45 +_INIT_PHASE_FULL_CYCLE = [0, 0.5, 0.5, 0] + + +def _setup_controller(robot: Any) -> locomotion_controller.LocomotionController: + """Creates the controller.""" + desired_speed = (0, 0) + desired_twisting_speed = 0 + + gait_generator = openloop_gait_generator.OpenloopGaitGenerator( + robot, + stance_duration=_STANCE_DURATION_SECONDS, + duty_factor=_DUTY_FACTOR, + initial_leg_phase=_INIT_PHASE_FULL_CYCLE) + state_estimator = com_velocity_estimator.COMVelocityEstimator(robot) + sw_controller = raibert_swing_leg_controller.RaibertSwingLegController( + robot, + gait_generator, + state_estimator, + desired_speed=desired_speed, + desired_twisting_speed=desired_twisting_speed, + desired_height=_BODY_HEIGHT, + ) + st_controller = torque_stance_leg_controller.TorqueStanceLegController( + robot, + gait_generator, + state_estimator, + desired_speed=desired_speed, + desired_twisting_speed=desired_twisting_speed, + desired_body_height=_BODY_HEIGHT, + body_mass=215 / 9.8, + body_inertia=(0.07335, 0, 0, 0, 0.25068, 0, 0, 0, 0.25447), + ) + + controller = locomotion_controller.LocomotionController( + robot=robot, + gait_generator=gait_generator, + state_estimator=state_estimator, + swing_leg_controller=sw_controller, + stance_leg_controller=st_controller, + clock=robot.GetTimeSinceReset) + return controller + + +def _update_controller_params( + controller: locomotion_controller.LocomotionController, + lin_speed: np.ndarray, ang_speed: float): + """Apply the desired speed and twisting speed.""" + controller.swing_leg_controller.desired_speed = lin_speed + controller.swing_leg_controller.desired_twisting_speed = ang_speed + controller.stance_leg_controller.desired_speed = lin_speed + controller.stance_leg_controller.desired_twisting_speed = ang_speed + + +@gin.configurable +class WalkingWrapper(object): + """Wraps a baseline walking controller for Laikago/Vision60.""" + + def __init__(self, + gym_env: Any, + action_repeat=20, + speed_bound=(-0.3, 0.3), + angular_speed_bound=(-0.3, 0.3)): + """Initialzes the wrapped env. + + Args: + gym_env: An instance of LocomotionGymEnv. + action_repeat: Number of control steps to run low level controller with + the high level inputs per step(). + speed_bound: The min/max of the input speed. + angular_speed_bound: The min/max of the twisting speed. + """ + self._gym_env = gym_env + self._controller = _setup_controller(self._gym_env.robot) + self._action_repeat = action_repeat + + action_low = np.array(speed_bound) + action_high = np.array(angular_speed_bound) + + # Overwrite the action space. + self.action_space = spaces.Box(action_low, action_high) + + def reset(self, *args, **kwargs) -> Any: + obs = self._gym_env.reset(*args, **kwargs) + # The robot instance might have been replaced if hard_reset is called. We + # just recreate the controller. + self._controller = _setup_controller(self._gym_env.robot) + self._controller.reset() + return obs + + def __getattr__(self, attr): + return getattr(self._gym_env, attr) + + def step(self, action) -> Any: + """Steps the wrapped env with high level commands. + + Args: + action: A high level command containing the desired linear and angular + speed of the robot base. The speed can be adjusted at any time. + + Returns: + The gym env observation, reward, termination, and additional info. + + """ + lin_speed = np.array((action[0], 0, 0)) + ang_speed = action[1] + _update_controller_params(self._controller, lin_speed, ang_speed) + for _ in range(self._action_repeat): + self._controller.update() + hybrid_action = self._controller.get_action() + obs, reward, done, info = self._gym_env.step(hybrid_action) + if done: + break + return obs, reward, done, info diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric.py new file mode 100644 index 000000000..00dea1800 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric.py @@ -0,0 +1,125 @@ +"""The metric reporting system used in our env. + +In the gym environment, there are many variables or quantities that can help +researchers to debug, evaluate policy performance. Such quantities may +include: Motor torques for quadrupeds, even when they are controlled in +POSITION mode; Distance to walls while a wheeled robot is navigating the +indoor environment. Often, these metrics are private variables (or can only be +computed from private variables). To expose the user interested metrics +outside the environment's observations, we designed this Metric system that +can be invoked in any modules (robot, tasks, sensors) inside the gym env. +""" + +import enum +import logging +from typing import Any, Callable, Dict, Sequence, Text +import gin + + +@gin.constants_from_enum +class MetricScope(enum.Enum): + """The supported scope of metrics.""" + # The performance metrics. + PERFORMANCE = 1, + + # The debug metrics for diagnostic purposes. + DEBUG = 2, + + # The safety metrics. + SAFETY = 3, + + # The statistics of episodes in metric format. + STATISTIC = 4, + + +class MetricCore(object): + """Aggregates values of interest to compute statistics.""" + + def __init__( + self, + name: Text, + scope: MetricScope, + single_ep_aggregator: Callable[[Sequence[Any]], Any], + multi_ep_aggregator: Callable[[Sequence[Any]], Dict[Text, Any]], + ): + """Initializes the class. + + Args: + name: The name of the metric, for example "motor_torques", + "distance_to_wall", etc. The full name of the metric will have scope + name in the prefix, i.e. "scope/name". + scope: The scope of this metric. Most metric should be for DEBUG purpose. + The scope name will be added to the final name of metric in this way: + "scope/name", which is standarded format for Tensorboard to group + named variables. + single_ep_aggregator: The function to process all aggregated metric + values. The derived MetricReporter (see below) will implements + reset_episode() which clears the episode data, and will be called during + env.reset(). + multi_ep_aggregator: The functions to process multi-episode metric values. + We assume the inputs to the functions is a list of per episode metric + values, i.e. each element of the list is the output from the + single_ep_aggregator. + """ + self._name = scope.name + "/" + name + self._single_ep_aggregator = single_ep_aggregator + self._multi_ep_aggregator = multi_ep_aggregator + self._episode_data = [] + + def report(self, metric_value: Any): + """Stores the reported metric in the internal buffer. + + Args: + metric_value: The metric we are interested to report. + """ + self._episode_data.append(metric_value) + + +class MetricReporter(MetricCore): + """Reports the metric using the provided aggregator functions.""" + + def get_episode_metric(self) -> Dict[Text, Sequence[Any]]: + """Processes and returns episode metric values. + + Returns: + Aggregated metrics for the current episode. + """ + if self._episode_data: + return {self._name: self._single_ep_aggregator(self._episode_data)} + else: + return {} + + def get_multi_ep_metric( + self, episodic_metric: Dict[Text, Sequence[Any]]) -> Dict[Text, Any]: + """Processes the aggregated metrics over many episodes. + + Will not be affected by reset_episode, since we take multi-episode data as + inputs. + + Args: + episodic_metric: The per episode metrics. We expect the inputs to contain + the same key as self._name, and that the value is a list of metric + values computed using self.get_episode_metrc(). + + Returns: + The processed multi-episode metrics. + """ + if self._name not in episodic_metric: + logging.warning( + "The inputs does not contain the key for the current metric: %s", + self._name) + return {} + outputs = {} + for key, val in self._multi_ep_aggregator( + episodic_metric[self._name]).items(): + outputs[self._name + "_" + key] = val + return outputs + + def reset_episode(self): + """Clears the episode data stored. + + Will be invoked during env.reset(). This will effect how get_episode_metric + gets computed. + + """ + self._episode_data = [] diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_logger.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_logger.py new file mode 100644 index 000000000..05d24b4e1 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_logger.py @@ -0,0 +1,155 @@ +# Lint as: python3 +"""The system to log and manage all metrics.""" + +import logging +from typing import Any, Callable, Dict, Sequence, Text, Union +import numpy as np + +from pybullet_envs.minitaur.envs_v2.evaluation import metric as metric_lib + + +def _merge_dict_throw_on_duplicates( + dict_1: Dict[Text, Any], + dict_2: Dict[Text, Any], +): + """Merge the contents of dict_2 to dict_1. + + Args: + dict_1: The dictionary to merge into. + dict_2: The dictionary to merge from. + + Raises: + KeyError: if duplicated keys are found. + """ + for key, val in dict_2.items(): + if key in dict_1: + raise KeyError(f"Duplicate key: {key} found in both " + f"dictionaries: {dict_1} {dict_2}.") + dict_1[key] = val + + +def common_stats(x: Sequence[Union[float, Sequence[float]]], + flatten_array=False): + out = x + if flatten_array: + # Deals with array of arrays. + out = np.concatenate(x).flatten() + return { + "mean": np.mean(out), + "max": np.max(out), + "min": np.min(out), + "std": np.std(out), + } + + +class MetricLogger(object): + """The central system to manage all metrics.""" + + def __init__(self, ignore_duplicate_metrics: bool = True): + """Initializes the system. + + Args: + ignore_duplicate_metrics: Don't throw error when users want to recreate + the same metric. + """ + self._metric_reporters = {} + self._ignore_duplicate_metrics = ignore_duplicate_metrics + + def reset_episode(self): + """Resets all metric reporters's internal buffer. + + Will be called by the gym env during reset. + """ + for metric in self._metric_reporters.values(): + metric.reset_episode() + + def create_metric( + self, + name: Text, + scope: metric_lib.MetricScope, + single_ep_aggregator: Callable[[Sequence[Any]], Any], + multi_ep_aggregator: Callable[[Sequence[Any]], Dict[Text, Any]], + ) -> metric_lib.MetricCore: + """Creates a new metric. + + Args: + name: The name of the metric, for example "motor_torques", + "distance_to_wall", etc. The full name of the metric will have scope + name in the prefix, i.e. "scope/name". + scope: The scope of this metric. Most metric should be for DEBUG purpose. + The scope name will be added to the final name of metric in this way: + "scope/name", which is standarded format for Tensorboard to group + named variables. + single_ep_aggregator: The function to process all aggregated metric + values. The derived MetricReporter (see below) will implements + reset_episode() which clears the episode data, and will be called during + env.reset(). + multi_ep_aggregator: The function to process multi-episode metric values. + We assume the inputs to the function is a list of per episode metric + values, i.e. each element of the list is the output from the + single_ep_aggregator. + + Returns: + A MetricCore which can be used to report values of interest. + """ + if name in self._metric_reporters: + if self._ignore_duplicate_metrics: + logging.warning("Trying to create an existing metric: %s", name) + name = self._get_valid_duplicate_name(name) + logging.warning("Changed duplicate metric to new name: %s", name) + else: + raise ValueError(f"Duplicated metrics found: {name}") + + self._metric_reporters[name] = metric_lib.MetricReporter( + name, scope, single_ep_aggregator, multi_ep_aggregator) + return self._metric_reporters[name] + + def create_scalar_metric( + self, + name: Text, + scope: metric_lib.MetricScope, + single_ep_aggregator: Callable[[Sequence[Any]], Any], + ) -> metric_lib.MetricCore: + """Shortcut to create a metric for scalar variables.""" + return self.create_metric( + name, + scope, + single_ep_aggregator, + multi_ep_aggregator=common_stats, + ) + + def _get_valid_duplicate_name(self, original_name: Text) -> Text: + counter = 1 + test_name = "{}_duplicate_{}".format(original_name, str(counter)) + while test_name in self._metric_reporters: + counter += 1 + test_name = "{}_duplicate_{}".format(original_name, str(counter)) + return test_name + + def get_episode_metrics(self) -> Dict[Text, Any]: + """Return all metrics registered in the logger for the current episode.""" + ep_stats = {} + for metric in self._metric_reporters.values(): + _merge_dict_throw_on_duplicates(ep_stats, metric.get_episode_metric()) + return ep_stats + + def get_multi_episode_metrics( + self, episodic_metrics: Dict[Text, Sequence[Any]]) -> Dict[Text, Any]: + """Processes the aggregated metrics over many episodes. + + Will not be affected by reset_episode, since we take multi-episode data as + inputs. + + Args: + episodic_metrics: The per episode metrics. For each key in the inputs, we + expect at least one metric reporter can process the corresponding value, + which contains a list of episodic metrics. + + Returns: + The processed multi-episode metrics. + """ + stats = {} + for metric in self._metric_reporters.values(): + _merge_dict_throw_on_duplicates( + stats, metric.get_multi_ep_metric(episodic_metrics)) + return stats diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_utils.py new file mode 100644 index 000000000..1defba977 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/evaluation/metric_utils.py @@ -0,0 +1,29 @@ +"""Helper class and functions to make computing meteric statistics easier.""" +from typing import Any, Sequence + +import numpy as np + + +class MetricStats(object): + """Helper class to make computing meteric statistics easier to manage.""" + + def __init__(self, data: Sequence[Any]): + if None or not list(data): + raise ValueError("Input data for ComputeMetricStats cannot be empty.") + self._data = np.asarray(data).flatten() + + @property + def avg(self): + return np.mean(self._data) + + @property + def min(self): + return np.min(self._data) + + @property + def max(self): + return np.max(self._data) + + @property + def sum(self): + return np.sum(self._data) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_mpc_wrapper_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_mpc_wrapper_example.py new file mode 100644 index 000000000..ad41f5633 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_mpc_wrapper_example.py @@ -0,0 +1,96 @@ +# Lint as: python3 +r"""An example where Laikago walks forward using mpc controller. + +This script illustrates an example of MPCLocomotionWrapper class for +controlling a simulated Laikago robot to walk on a flat ground. The wrapped +environment takes as input the target local foothold locations and the desired +base pose. An MPC-based controller is executed internally to compute the +required forces to achieve the desired foothold position and base movement. + + +blaze run -c opt \ +//robotics/reinforcement_learning/minitaur/envs_v2/examples\ +:laikago_mpc_wrapper_example +""" +import os +import tempfile + +from absl import app +from absl import flags +import gin +import numpy as np +import pybullet_data as pd + +from pybullet_envs.minitaur.envs_v2 import env_loader + +FLAGS = flags.FLAGS +flags.DEFINE_string("video_file", None, "The filename for saving the videos.") + +CONFIG_FILE_SIM = (pd.getDataPath()+"/configs/laikago_mpc_example_flat.gin") + +NUM_STEPS = 100 +ENABLE_RENDERING = True # Will be disabled for tests +ENV_RANDOM_SEED = 100 +DEFAULT_TARGET_FOOTHOLD = (0.05, 0.0, -0.01) +DEFAULT_BASE_VELOCITY = (0.0, 0.0) +DEFAULT_TWIST_SPEED = 0.0 +DEFAULT_BODY_HEIGHT = 0.45 +DEFAULT_ROLL_PITCH = (0.0, 0.0) +DEFAULT_SWING_HEIGHT = 0.07 + + +def _build_env(): + """Builds the environment for the Laikago robot. + + Returns: + The OpenAI gym environment. + """ + gin.parse_config_file(CONFIG_FILE_SIM) + gin.bind_parameter("SimulationParameters.enable_rendering", ENABLE_RENDERING) + env = env_loader.load() + env.seed(ENV_RANDOM_SEED) + + return env + + +def _run_example(): + """An example that Laikago moves with a constant speed and predicts foothold. + + Returns: + env: the environment after the simulation + """ + + env = _build_env() + + env.reset() + if FLAGS.video_file is not None: + pybullet = env.pybullet_client + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0) + log_id = pybullet.startStateLogging(pybullet.STATE_LOGGING_VIDEO_MP4, + FLAGS.video_file) + + try: + max_step = NUM_STEPS + for _ in range(max_step): + target_foothold = np.array(DEFAULT_TARGET_FOOTHOLD) + action = np.concatenate([ + target_foothold, target_foothold, target_foothold, target_foothold, + [ + DEFAULT_SWING_HEIGHT, DEFAULT_SWING_HEIGHT, DEFAULT_SWING_HEIGHT, + DEFAULT_SWING_HEIGHT + ], DEFAULT_BASE_VELOCITY, [DEFAULT_TWIST_SPEED], + [DEFAULT_BODY_HEIGHT], DEFAULT_ROLL_PITCH + ]) + _ = env.step(action) + finally: + if FLAGS.video_dir is not None: + pybullet.stopStateLogging(log_id) + + +def main(argv): + del argv + _run_example() + + +if __name__ == "__main__": + app.run(main) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_pmtg_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_pmtg_example.py new file mode 100644 index 000000000..c86c10db4 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_pmtg_example.py @@ -0,0 +1,84 @@ +r"""An example to run an OpenAI gym environment with laikago and PMTG wrapper. + +This is an open-loop controller where we use 0 residuals and only execute the +trajectory generator. The parameters that are (normally modulated by the policy) +are fixed (except the intensity) and not optimized. They are hand picked as +follows: + - The gait cycle frequency is 3 Hz. + - Walking height is neutral (0). + - Swing vs stance ratio is 2 (swing takes half the time vs stance). + - Intensity starts from zero and is gradually increased over time. + +blaze run -c opt \ +//robotics/reinforcement_learning/minitaur/envs_v2/examples\ +:laikago_pmtg_example +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import gin +import tensorflow.compat.v1 as tf +from pybullet_envs.minitaur.envs_v2 import env_loader +import pybullet_data as pd + +CONFIG_DIR = pd.getDataPath()+"/configs_v2/" +CONFIG_FILES = [ + os.path.join(CONFIG_DIR, "base/laikago_with_imu.gin"), + os.path.join(CONFIG_DIR, "tasks/fwd_task_no_termination.gin"), + os.path.join(CONFIG_DIR, "wrappers/pmtg_wrapper.gin"), + os.path.join(CONFIG_DIR, "scenes/simple_scene.gin") +] +# Constants used to show example PMTG behavior with zero residual. +# Since we use the default PMTG configuration, there is only one trajectory +# generator. So the parameters that can be changed are: +# - Multiplier for delta time (similar to frequency but per time step). +# - Intensity of the trajectory generator. +# - Walking heights used for the legs. +# - The ratio of the speed of the leg during swing vs stance phase. +# For more details, check out TgSimple._process_tg_params method. +_PMTG_DELTA_TIME_MULTIPLIER = 2.0 +_PMTG_INTENSITY_RANGE = (0.0, 1.5) +_PMTG_INTENSITY_STEP_SIZE = 0.0001 +_PMTG_WALKING_HEIGHT = -0.3 +_PMTG_SWING_VS_STANCE = 2 +_NUM_MOTORS = 12 + + +def main(argv): + del argv # Unused. + + # Parse the gym config and create the environment. + for gin_file in CONFIG_FILES: + gin.parse_config_file(gin_file) + gin.bind_parameter("SimulationParameters.enable_rendering", True) + gin.bind_parameter("terminal_conditions.maxstep_terminal_condition.max_step", + 10000) + env = env_loader.load() + tg_intensity = _PMTG_INTENSITY_RANGE[0] + sum_reward = 0 + env.reset() + done = False + # Use zero residual, only use the output of the trajectory generator. + residual = [0] * _NUM_MOTORS + # Since we fix residuals and all the parameters of the TG, this example + # is practically an open loop controller. A learned policy would provide + # different values for these parameters at every timestep. + while not done: + # Increase the intensity of the trajectory generator gradually + # to illustrate increasingly larger steps. + if tg_intensity < _PMTG_INTENSITY_RANGE[1]: + tg_intensity += _PMTG_INTENSITY_STEP_SIZE + tg_params = [ + _PMTG_DELTA_TIME_MULTIPLIER, tg_intensity, _PMTG_WALKING_HEIGHT, + _PMTG_SWING_VS_STANCE + ] + action = residual + tg_params + _, reward, done, _ = env.step(action) + sum_reward += reward + + +if __name__ == "__main__": + tf.app.run(main) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_static_gait_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_static_gait_example.py new file mode 100644 index 000000000..13e2387cc --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/laikago_static_gait_example.py @@ -0,0 +1,52 @@ +# Lint as: python3 +r"""An example that the Laikago walks forward using a static gait. + +blaze run -c opt //robotics/reinforcement_learning/minitaur/envs_v2/examples:\ +laikago_static_gait_example +""" +from absl import app +from absl import flags +import gin +from pybullet_envs.minitaur.agents.baseline_controller import static_gait_controller +from pybullet_envs.minitaur.envs_v2 import env_loader +import pybullet_data as pd + +flags.DEFINE_bool("render", True, "Whether to render the example.") + +FLAGS = flags.FLAGS +_CONFIG_FILE = pd.getDataPath()+"/configs/laikago_walk_static_gait.gin" +_NUM_STEPS = 10000 +_ENV_RANDOM_SEED = 13 + + +def _load_config(render=False): + gin.parse_config_file(_CONFIG_FILE) + gin.bind_parameter("SimulationParameters.enable_rendering", render) + + +def run_example(num_max_steps=_NUM_STEPS): + """Runs the example. + + Args: + num_max_steps: Maximum number of steps this example should run for. + """ + env = env_loader.load() + + env.seed(_ENV_RANDOM_SEED) + observation = env.reset() + policy = static_gait_controller.StaticGaitController(env.robot) + + for _ in range(num_max_steps): + action = policy.act(observation) + _, _, done, _ = env.step(action) + if done: + break + + +def main(_): + _load_config(FLAGS.render) + run_example() + + +if __name__ == "__main__": + app.run(main) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config.py new file mode 100644 index 000000000..7b9ad955a --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config.py @@ -0,0 +1,58 @@ +"""A gin-config class for locomotion_gym_env. + +This should be identical to locomotion_gym_config.proto. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import Sequence, Text + +import attr +import gin + + +@gin.configurable +@attr.s +class SimulationParameters(object): + """Parameters specific for the pyBullet simulation.""" + sim_time_step_s = attr.ib(type=float, default=0.002) + num_action_repeat = attr.ib(type=int, default=5) + enable_hard_reset = attr.ib(type=bool, default=False) + enable_rendering = attr.ib(type=bool, default=False) + enable_rendering_gui = attr.ib(type=bool, default=True) + robot_on_rack = attr.ib(type=bool, default=False) + camera_target = attr.ib(type=Sequence[float], default=None) + camera_distance = attr.ib(type=float, default=1.0) + camera_yaw = attr.ib(type=float, default=0) + camera_pitch = attr.ib(type=float, default=-30) + render_width = attr.ib(type=int, default=480) + render_height = attr.ib(type=int, default=360) + egl_rendering = attr.ib(type=bool, default=False) + + +@gin.configurable +@attr.s +class ScalarField(object): + """A named scalar space with bounds.""" + # TODO(sehoonha) extension to vector fields. + name = attr.ib(type=str) + upper_bound = attr.ib(type=float) + lower_bound = attr.ib(type=float) + + +@gin.configurable +@attr.s +class LocomotionGymConfig(object): + """Grouped Config Parameters for LocomotionGym.""" + simulation_parameters = attr.ib(type=SimulationParameters) + # TODO(sehoonha) implement attr validators for the list + actions = attr.ib(type=list, default=None) # pylint: disable=g-bare-generic + log_path = attr.ib(type=Text, default=None) + data_dir = attr.ib( + type=Text, + default='robotics/reinforcement_learning/minitaur/data/') + profiling_path = attr.ib(type=Text, default=None) + seed = attr.ib(type=int, default=None) + ignored_sensor_list = attr.ib(type=Sequence[Text], default=()) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config_test.py new file mode 100644 index 000000000..fc53f02e7 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_config_test.py @@ -0,0 +1,75 @@ +"""Tests for pybullet_envs.minitaur.envs.locomotion_gym_config.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gin +from pybullet_envs.minitaur.envs_v2 import locomotion_gym_config +import tensorflow.compat.v1 as tf +from absl.testing import parameterized + + + +class LocomotionGymConfigTest(tf.test.TestCase, parameterized.TestCase): + + def testSimulationParametersFromGinString(self): + config_text = ( + 'import pybullet_envs.minitaur' + '.envs_v2.locomotion_gym_config\n' + 'locomotion_gym_config.SimulationParameters.sim_time_step_s = 0.005\n' + 'locomotion_gym_config.SimulationParameters.camera_distance = 5.0\n' + 'locomotion_gym_config.SimulationParameters.camera_yaw = 10\n' + 'locomotion_gym_config.SimulationParameters.camera_pitch = -50\n' + ) + gin.parse_config(config_text) + + cfg = locomotion_gym_config.SimulationParameters() + self.assertEqual(cfg.sim_time_step_s, 0.005) + self.assertFalse(cfg.enable_hard_reset) + self.assertEqual(cfg.camera_distance, 5.0) + self.assertEqual(cfg.camera_yaw, 10) + self.assertEqual(cfg.camera_pitch, -50) + + def testScalarFieldFromGinString(self): + config_text = ( + 'import pybullet_envs.minitaur' + '.envs_v2.locomotion_gym_config\n' + 'locomotion_gym_config.ScalarField.name = "MotorUpperLimit"\n' + 'locomotion_gym_config.ScalarField.upper_bound = 1.0\n' + 'locomotion_gym_config.ScalarField.lower_bound = -1.0\n' + ) + gin.parse_config(config_text) + + cfg = locomotion_gym_config.ScalarField() + self.assertEqual(cfg.name, 'MotorUpperLimit') + self.assertEqual(cfg.upper_bound, 1.0) + self.assertEqual(cfg.lower_bound, -1.0) + + def testLocomotionGymConfigFromGinString(self): + config_text = ( + 'import pybullet_envs.minitaur' + '.envs_v2.locomotion_gym_config\n' + # SimulationParameters + 'locomotion_gym_config.SimulationParameters.sim_time_step_s = 0.005\n' + # Actions + 'Motor/locomotion_gym_config.ScalarField.name = "MotorUpperLimit"\n' + 'Motor/locomotion_gym_config.ScalarField.upper_bound = 1.0\n' + 'Motor/locomotion_gym_config.ScalarField.lower_bound = -1.0\n' + # LocomotionGymConfigs + 'locomotion_gym_config.LocomotionGymConfig.simulation_parameters = ' + '@locomotion_gym_config.SimulationParameters()\n' + 'locomotion_gym_config.LocomotionGymConfig.actions = [' + '@Motor/locomotion_gym_config.ScalarField()]\n' + 'locomotion_gym_config.LocomotionGymConfig.ignored_sensor_list = [' + '"Collisions"]\n') + gin.parse_config(config_text) + + cfg = locomotion_gym_config.LocomotionGymConfig() + self.assertEqual(cfg.simulation_parameters.sim_time_step_s, 0.005) + self.assertEqual(cfg.actions[0].upper_bound, 1.0) + self.assertEqual(cfg.ignored_sensor_list, ['Collisions']) + + +if __name__ == '__main__': + tf.test.main() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env.py new file mode 100644 index 000000000..0c30e5b8e --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env.py @@ -0,0 +1,731 @@ +# Lint as: python3 +"""This file implements the locomotion gym env.""" + +import atexit +import collections +import time +from typing import Any, Callable, Sequence, Text, Union +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2 import base_client +from pybullet_utils import bullet_client +import pybullet_data +import pybullet +from pybullet_envs.minitaur.envs import minitaur_logging +from pybullet_envs.minitaur.envs import minitaur_logging_pb2 +#from pybullet_envs.minitaur.envs import minitaur_logging +#from pybullet_envs.minitaur.envs import minitaur_logging_pb2 +from pybullet_envs.minitaur.envs_v2.evaluation import metric_logger +from pybullet_envs.minitaur.envs_v2.scenes import scene_base +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.sensors import space_utils +from pybullet_envs.minitaur.envs_v2.utilities import rendering_utils +from pybullet_envs.minitaur.robots import autonomous_object +from pybullet_envs.minitaur.robots import robot_base + +_ACTION_EPS = 0.01 +_NUM_SIMULATION_ITERATION_STEPS = 300 +_LOG_BUFFER_LENGTH = 5000 + +SIM_CLOCK = 'SIM_CLOCK' + +# Exports this symbol so we can use it in the config file. +gin.constant('locomotion_gym_env.SIM_CLOCK', SIM_CLOCK) + +# This allows us to bind @time.time in the gin configuration. +gin.external_configurable(time.time, module='time') + + +# TODO(b/122048194): Enable position/torque/hybrid control mode. +@gin.configurable +class LocomotionGymEnv(gym.Env): + """The gym environment for the locomotion tasks.""" + metadata = { + 'render.modes': ['human', 'rgb_array', 'topdown'], + 'video.frames_per_second': 100 + } + + def __init__(self, + gym_config, + clock: Union[Callable[..., float], Text] = 'SIM_CLOCK', + robot_class: Any = None, + scene: scene_base.SceneBase = None, + sensors: Sequence[sensor.Sensor] = None, + task: Any = None, + env_randomizers: Any = None): + """Initializes the locomotion gym environment. + + Args: + gym_config: An instance of LocomotionGymConfig. + clock: The clock source to be used for the gym env. The clock should + return a timestamp in seconds. Setting clock == "SIM_CLOCK" will enable + the built-in simulation clock. For real robot experiments, we can use + time.time or other clock wall clock sources. + robot_class: A class of a robot. We provide a class rather than an + instance due to hard_reset functionality. Parameters are expected to be + configured with gin. + scene: An object for managing the robot's surroundings. + sensors: A list of environmental sensors for observation. + task: A callable function/class to calculate the reward and termination + condition. Takes the gym env as the argument when calling. + env_randomizers: A list of EnvRandomizer(s). An EnvRandomizer may + randomize the physical property of minitaur, change the terrrain during + reset(), or add perturbation forces during step(). + client_factory: A function to create a simulation client, it can be a + pybullet client. + + Raises: + ValueError: If the num_action_repeat is less than 1. + + """ + self._pybullet_client = None + self._metric_logger = metric_logger.MetricLogger() + # TODO(sehoonha) split observation and full-state sensors (b/129858214) + + # Makes sure that close() is always called to flush out the logs to the + # disk. + atexit.register(self.close) + self.seed() + self._gym_config = gym_config + if robot_class is None: + raise ValueError('robot_class cannot be None.') + self._robot_class = robot_class + if issubclass(self._robot_class, robot_base.RobotBase): + self._use_new_robot_class = True + else: + self._use_new_robot_class = False + self._robot = None + + self._scene = scene or scene_base.SceneBase() + + # TODO(sehoonha) change the data structure to dictionary + self._env_sensors = list(sensors) if sensors is not None else list() + + # TODO(b/152161457): Make logging a standalone module. + self._log_path = gym_config.log_path + self._logging = minitaur_logging.MinitaurLogging(self._log_path) + self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() + self._data_dir = gym_config.data_dir + + self._task = task + + self._env_randomizers = env_randomizers if env_randomizers else [] + + # Simulation related parameters. + self._num_action_repeat = gym_config.simulation_parameters.num_action_repeat + self._on_rack = gym_config.simulation_parameters.robot_on_rack + if self._num_action_repeat < 1: + raise ValueError('number of action repeats should be at least 1.') + # TODO(b/73829334): Fix the value of self._num_bullet_solver_iterations. + self._num_bullet_solver_iterations = int(_NUM_SIMULATION_ITERATION_STEPS / + self._num_action_repeat) + + self._sim_time_step = gym_config.simulation_parameters.sim_time_step_s + # The sim step counter is an internal varialbe to count the number of + # pybullet stepSimulation() has been called since last reset. + self._sim_step_counter = 0 + + self._env_time_step = self._num_action_repeat * self._sim_time_step + # The env step counter accounts for how many times env.step has been + # called since reset. + self._env_step_counter = 0 + + if clock == SIM_CLOCK: + self._clock = self._get_sim_time + else: + self._clock = clock + + # Creates the bullet client. + self._is_render = gym_config.simulation_parameters.enable_rendering + # The wall-clock time at which the last frame is rendered. + self._last_frame_time = 0.0 + + if gym_config.simulation_parameters.enable_rendering: + self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI) + self._pybullet_client.configureDebugVisualizer( + pybullet.COV_ENABLE_GUI, + gym_config.simulation_parameters.enable_rendering_gui) + else: + self._pybullet_client = bullet_client.BulletClient() + if gym_config.simulation_parameters.egl_rendering: + self._pybullet_client.loadPlugin('eglRendererPlugin') + + self._pybullet_client.setAdditionalSearchPath( + pybullet_data.getDataPath()) + + # If enabled, save the performance profile to profiling_path + # Use Google Chrome about://tracing to open the file + if gym_config.profiling_path is not None: + self._profiling_slot = self._pybullet_client.startStateLogging( + self._pybullet_client.STATE_LOGGING_PROFILE_TIMINGS, + gym_config.profiling_path) + self._profiling_counter = 10 + else: + self._profiling_slot = -1 + + # Set the default render options. TODO(b/152161124): Make rendering a + # standalone module. + self._camera_target = gym_config.simulation_parameters.camera_target + self._camera_dist = gym_config.simulation_parameters.camera_distance + self._camera_yaw = gym_config.simulation_parameters.camera_yaw + self._camera_pitch = gym_config.simulation_parameters.camera_pitch + self._render_width = gym_config.simulation_parameters.render_width + self._render_height = gym_config.simulation_parameters.render_height + + # Loads the environment and robot. Actions space will be created as well. + self._hard_reset = True + self._observation_dict = {} + self.reset() + self._hard_reset = gym_config.simulation_parameters.enable_hard_reset + + # Construct the observation space from the list of sensors. + self.observation_space = ( + space_utils.convert_sensors_to_gym_space_dictionary([ + sensor for sensor in self.all_sensors() + if sensor.get_name() not in self._gym_config.ignored_sensor_list + ])) + + def __del__(self): + self.close() + + def _load_old_robot_class(self): + self._robot = self._robot_class( + pybullet_client=self._pybullet_client, on_rack=self._on_rack) + self._action_list = [] + action_upper_bound = [] + action_lower_bound = [] + for action in self._gym_config.actions: + self._action_list.append(action.name) + action_upper_bound.append(action.upper_bound) + action_lower_bound.append(action.lower_bound) + self.action_space = gym.spaces.Box( + np.array(action_lower_bound), + np.array(action_upper_bound), + dtype=np.float32) + + def _load_new_robot_class(self): + self._robot = self._robot_class( + pybullet_client=self._pybullet_client, clock=self._clock) + self.action_space = self._robot.action_space + + def _load(self): + self._pybullet_client.resetSimulation() + self._pybullet_client.setPhysicsEngineParameter( + numSolverIterations=self._num_bullet_solver_iterations) + self._pybullet_client.setTimeStep(self._sim_time_step) + self._pybullet_client.setGravity(0, 0, -10) + self._pybullet_client.setPhysicsEngineParameter(enableConeFriction=0) + + # Disable rendering during scene loading will speed up simulation. + if self._is_render: + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_RENDERING, 0) + + # Rebuild the scene. + self._scene.build_scene(self._pybullet_client) + + # TODO(b/151975607): Deprecate old robot support. + if self._use_new_robot_class: + self._load_new_robot_class() + else: + self._load_old_robot_class() + + # Check action space. + if (isinstance(self.action_space, gym.spaces.Box) and + not np.all(self.action_space.low < self.action_space.high)): + raise ValueError(f'Action space contains invalid dimensions, ' + f'action space low = {self.action_space.low}, ' + f'action space high = {self.action_space.high}') + + for an_object in self._dynamic_objects(): + an_object.set_clock(self._clock) + + # Enable rendering after loading finishes. + if self._is_render: + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_RENDERING, 1) + + def close(self): + atexit.unregister(self.close) + #if self._pybullet_client: + + if self._log_path is not None: + self._logging.save_episode(self._episode_proto) + for sensor_ in self.all_sensors(): + sensor_.on_terminate(self) + if self._robot: + if self._use_new_robot_class: + self._robot.terminate() + else: + self._robot.Terminate() + if self._pybullet_client: + self._pybullet_client.disconnect() + self._pybullet_client = None + + def seed(self, seed=None): + self.np_random, self.np_random_seed = gym.utils.seeding.np_random(seed) + return [self.np_random_seed] + + def _dynamic_objects(self): + """Returns the python objects controlling moving obstacles.""" + if self._scene: + return self._scene.dynamic_objects + else: + return [] + + def all_sensors(self): + """Returns all robot, environmental and dynamic objects sensors.""" + if self._use_new_robot_class: + all_sensors = list(self._env_sensors) + if self._robot: + all_sensors.extend(list(self._robot.sensors)) + for obj in self._dynamic_objects(): + all_sensors.extend(obj.sensors) + + # The new way of adding task specific sensors to the sensor lists. + if hasattr(self._task, 'sensors'): + all_sensors.extend(self._task.sensors) + return all_sensors + else: + # This is a workaround due to the issue in b/130128505#comment5 + task_sensor = ([self._task] + if isinstance(self._task, sensor.Sensor) else []) + robot_sensors = [] + if self._robot: + robot_sensors = self._robot.GetAllSensors() + return robot_sensors + self._env_sensors + task_sensor + + def sensor_by_name(self, name): + """Returns the sensor with the given name, or None if not exist.""" + # TODO(b/154162104): Store sensors as dictionary. + for sensor_ in self.all_sensors(): + if sensor_.get_name() == name: + return sensor_ + return None + + @gin.configurable('locomotion_gym_env.LocomotionGymEnv.reset') + def reset( + self, + initial_motor_angles=None, + reset_duration=1.0, + reset_visualization_camera=True, + ): + """Resets the robot's position in the world or rebuild the sim world. + + The simulation world will be rebuilt if self._hard_reset is True. + + Args: + initial_motor_angles: A list of Floats. The desired joint angles after + reset. If None, the robot will use its built-in value. + reset_duration: Float. The time (in seconds) needed to rotate all motors + to the desired initial values. + reset_visualization_camera: Whether to reset debug visualization camera on + reset. + + Returns: + A numpy array contains the initial observation after reset. + """ + + + self._env_step_counter = 0 + self._sim_step_counter = 0 + self._last_reset_time = self._clock() + self._metric_logger.reset_episode() + + # Clear the simulation world and rebuild the robot interface. + if self._hard_reset: + self._load() + + # Resets the scene + self._scene.reset() + + # Resets the robot with the provided init parameters. + if self._use_new_robot_class: + self._robot.reset() + else: + self._robot.Reset( + reload_urdf=False, + default_motor_angles=initial_motor_angles, + reset_time=reset_duration) + + # Flush the logs to disc and reinitialize the logging system. + if self._log_path is not None: + self._logging.save_episode(self._episode_proto) + self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() + minitaur_logging.preallocate_episode_proto(self._episode_proto, + _LOG_BUFFER_LENGTH, + self._robot) + + # TODO(b/152161124): Move this part to the renderer module. + if reset_visualization_camera: + self._pybullet_client.resetDebugVisualizerCamera(self._camera_dist, + self._camera_yaw, + self._camera_pitch, + [0, 0, 0]) + + # Create an example last action based on the type of action space. + self._last_action = space_utils.create_constant_action(self.action_space) + + for s in self.all_sensors(): + s.on_reset(self) + + if self._task and hasattr(self._task, 'reset'): + self._task.reset(self) + + # Loop over all env randomizers. + for env_randomizer in self._env_randomizers: + env_randomizer.randomize_env(self) + + for obj in self._dynamic_objects(): + obj.reset() + + # Initialize the robot base position. + if self._use_new_robot_class: + self._last_base_position = self._robot.base_position + else: + self._last_base_position = self._robot.GetBasePosition() + + # Resets the observations again, since randomizers might change the env. + for s in self.all_sensors(): + s.on_reset(self) + + + self._last_reset_time = self._clock() + return self._get_observation() + + def _wait_for_rendering(self): + # Sleep, otherwise the computation takes less time than real time, + # which will make the visualization like a fast-forward video. + time_spent = time.time() - self._last_frame_time + self._last_frame_time = time.time() + time_to_sleep = self._env_time_step - time_spent + if time_to_sleep > 0: + time.sleep(time_to_sleep) + + # Also keep the previous orientation of the camera set by the user. + [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] + self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, + self._last_base_position) + + def _step_old_robot_class(self, action): + self._last_base_position = self._robot.GetBasePosition() + self._last_action = action + + if self._is_render: + self._wait_for_rendering() + + for env_randomizer in self._env_randomizers: + env_randomizer.randomize_step(self) + + self._robot.Step(action) + + if self._profiling_slot >= 0: + self._profiling_counter -= 1 + if self._profiling_counter == 0: + self._pybullet_client.stopStateLogging(self._profiling_slot) + + if self._log_path is not None: + minitaur_logging.update_episode_proto(self._episode_proto, self._robot, + action, self._env_step_counter) + reward = self._reward() + + for s in self.all_sensors(): + s.on_step(self) + + if self._task and hasattr(self._task, 'update'): + self._task.update(self) # TODO(b/154635313): resolve API mismatch + + done = self._termination() + self._env_step_counter += 1 + # TODO(b/161941829): terminate removed for now, change terminate to other + # names. + return self._get_observation(), reward, done, {} + + def _step_new_robot_class(self, action): + self._last_base_position = self._robot.base_position + self._last_action = action + + if self._is_render: + self._wait_for_rendering() + + for env_randomizer in self._env_randomizers: + env_randomizer.randomize_step(self) + + action = self._robot.pre_control_step(action, self._env_time_step) + for obj in self._dynamic_objects(): + obj.pre_control_step(autonomous_object.AUTONOMOUS_ACTION) + for _ in range(self._num_action_repeat): + self._robot.apply_action(action) + for obj in self._dynamic_objects(): + obj.update(self.get_time_since_reset(), self._observation_dict) + obj.apply_action(autonomous_object.AUTONOMOUS_ACTION) + + self._pybullet_client.stepSimulation() + self._sim_step_counter += 1 + + self._robot.receive_observation() + for obj in self._dynamic_objects(): + obj.receive_observation() + + for s in self.all_sensors(): + s.on_new_observation() + + self._robot.post_control_step() + for obj in self._dynamic_objects(): + obj.post_control_step() + + if self._profiling_slot >= 0: + self._profiling_counter -= 1 + if self._profiling_counter == 0: + self._pybullet_client.stopStateLogging(self._profiling_slot) + + if self._log_path is not None: + minitaur_logging.update_episode_proto(self._episode_proto, self._robot, + action, self._env_step_counter) + reward = self._reward() + + for s in self.all_sensors(): + s.on_step(self) + + if self._task and hasattr(self._task, 'update'): + self._task.update(self) # TODO(b/154635313): resolve API mismatch + + done = self._termination() + self._env_step_counter += 1 + # TODO(b/161941829): terminate removed for now, change terminate to other + # names. + return self._get_observation(), reward, done, {} + + def step(self, action): + """Step forward the simulation, given the action. + + Args: + action: Can be a list of desired motor angles for all motors when the + robot is in position control mode; A list of desired motor torques. Or a + list of tuples (q, qdot, kp, kd, tau) for hybrid control mode. The + action must be compatible with the robot's motor control mode. Also, we + are not going to use the leg space (swing/extension) definition at the + gym level, since they are specific to Minitaur. + + Returns: + observations: The observation dictionary. The keys are the sensor names + and the values are the sensor readings. + reward: The reward for the current state-action pair. + done: Whether the episode has ended. + info: A dictionary that stores diagnostic information. + + Raises: + ValueError: The action dimension is not the same as the number of motors. + ValueError: The magnitude of actions is out of bounds. + """ + # TODO(b/151975607): Finish the migration and remove old robot class + # support. + if self._use_new_robot_class: + return self._step_new_robot_class(action) + else: + return self._step_old_robot_class(action) + + @gin.configurable('locomotion_gym_env.LocomotionGymEnv.render') + def render(self, mode='rgb_array'): + + if mode == 'topdown': + # Provide ground height if we know it. Otherwise leave it as gin + # configurable. + if hasattr(self.scene, 'ground_height'): + return rendering_utils.render_topdown( + self._pybullet_client, ground_height=self.scene.ground_height) + else: + return rendering_utils.render_topdown(self._pybullet_client) + + if mode != 'rgb_array': + raise ValueError('Unsupported render mode:{}'.format(mode)) + + if self._camera_target is not None: + target_position = self._camera_target + else: + target_position = self._last_base_position + view_matrix = self._pybullet_client.computeViewMatrixFromYawPitchRoll( + cameraTargetPosition=target_position, + distance=self._camera_dist, + yaw=self._camera_yaw, + pitch=self._camera_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( + fov=60, + aspect=float(self._render_width) / self._render_height, + nearVal=0.1, + farVal=100.0) + return rendering_utils.render_image(self._pybullet_client, + self._render_width, self._render_height, + view_matrix, proj_matrix) + + @property + def scene(self): + return self._scene + + @property + def rendering_enabled(self): + return self._is_render + + @property + def env_randomizers(self): + return self._env_randomizers + + @property + def last_base_position(self): + return self._last_base_position + + @property + def gym_config(self): + return self._gym_config + + def _termination(self): + if not self._robot.is_safe: + return True + + if self._task and hasattr(self._task, 'done'): + return self._task.done(self) # TODO(b/154635313): resolve API mismatch + + return False + + def _reward(self): + if self._task: + return self._task.reward(self) # TODO(b/154635313): resolve API mismatch + return 0 + + def _get_observation(self): + """Get observation of this environment from a list of sensors. + + Returns: + observations: dictionary of sensory observation with sensor name as key + and corresponding observation in numpy array as value. + """ + sensors_dict = {} + for s in self.all_sensors(): + if s.get_name() in self._gym_config.ignored_sensor_list: + continue + + obs = s.get_observation() + if isinstance(obs, dict): + sensors_dict.update(obs) + else: + sensors_dict[s.get_name()] = obs + + self._observation_dict = collections.OrderedDict( + sorted(sensors_dict.items())) + return self._observation_dict + + def set_time_step(self, num_action_repeat, sim_step=0.001): + """Sets the time step of the environment. + + Args: + num_action_repeat: The number of simulation steps/action repeats to be + executed when calling env.step(). + sim_step: The simulation time step in PyBullet. By default, the simulation + step is 0.001s, which is a good trade-off between simulation speed and + accuracy. + + Raises: + ValueError: If the num_action_repeat is less than 1. + """ + if num_action_repeat < 1: + raise ValueError('number of action repeats should be at least 1.') + self._sim_time_step = sim_step + self._num_action_repeat = num_action_repeat + self._env_time_step = sim_step * num_action_repeat + self._num_bullet_solver_iterations = int( + _NUM_SIMULATION_ITERATION_STEPS / self._num_action_repeat) + self._pybullet_client.setPhysicsEngineParameter( + numSolverIterations=self._num_bullet_solver_iterations) + self._pybullet_client.setTimeStep(self._sim_time_step) + if not self._use_new_robot_class: + self._robot.SetTimeSteps(self._num_action_repeat, self._sim_time_step) + + def _get_sim_time(self): + """Returns the simulation time since the sim resets.""" + return self._sim_step_counter * self._sim_time_step + + def get_time_since_reset(self): + """Get the time passed (in seconds) since the last reset. + + Returns: + Time in seconds since the last reset. + """ + if self._use_new_robot_class: + return self._clock() - self._last_reset_time + else: + return self._robot.GetTimeSinceReset() + + def get_time(self): + """Gets the time reading from the clock source.""" + return self._clock() + + @property + def observation(self): + return self._observation_dict + + @property + def pybullet_client(self): + return self._pybullet_client + + @property + def robot(self): + return self._robot + + @property + def num_action_repeat(self): + return self._num_action_repeat + + @property + def sim_time_step(self): + return self._sim_time_step + + @property + def env_step_counter(self): + return self._env_step_counter + + @property + def hard_reset(self): + return self._hard_reset + + @property + def last_action(self): + return self._last_action + + @property + def env_time_step(self): + return self._env_time_step + + @property + def data_dir(self): + return self._data_dir + + @property + def task(self): + return self._task + + @property + def robot_class(self): + return self._robot_class + + @property + def action_names(self): + """Name of each action in the action space. + + By default this returns the actions the robot executes (e.g. + "VELOCITY_elbow_joint"), but env wrappers may override this if they change + the action space (e.g. if they convert twist to motor commands). + + Returns: + Tuple of strings, the action names. + """ + if self._use_new_robot_class: + return self._robot.action_names + return self._action_list + + @property + def metric_logger(self): + return self._metric_logger diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env_test.py new file mode 100644 index 000000000..51f3c9ae6 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/locomotion_gym_env_test.py @@ -0,0 +1,241 @@ +# Lint as: python3 +r"""Tests for locomotion_gym_env. + + +""" + +import math +import random + +import gin +import mock +import numpy as np +import tensorflow.compat.v1 as tf +from absl.testing import parameterized + +from pybullet_envs.minitaur.envs_v2 import locomotion_gym_env +from pybullet_envs.minitaur.envs_v2.evaluation import metric as metric_lib +from pybullet_envs.minitaur.envs_v2.scenes import scene_base +from pybullet_envs.minitaur.envs_v2.scenes import simple_scene +from pybullet_envs.minitaur.envs_v2.tasks import task_interface +from pybullet_envs.minitaur.envs_v2.utilities import env_utils +from pybullet_envs.minitaur.robots import autonomous_object +from pybullet_envs.minitaur.robots import minitaur_v2 +import pybullet_data as pd + +import unittest + + +_POSITION_GAIN = 1.0 +_VELOCITY_GAIN = 0.015 +_CONTROL_LATENCY = 0.015 +_CONFIG_FILE = (pd.getDataPath()+'/configs/minitaur_gym_env.gin') +_CONFIG_FILE_NEW_ROBOT = (pd.getDataPath()+'/configs_v2/base/laikago_reactive.gin') + + +class TestTask(task_interface.Task): + """A step counter task for test purpose.""" + + def __init__(self): + self._counter = 0 + + def reset(self, env): + del env + self._counter = 0 + + def reward(self, env): + del env # TODO(b/154635313): resolve the API mismatch + self._counter += 1 + return self._counter + + def update(self, env): + del env # TODO(b/154635313): resolve the API mismatch + + def done(self, env): + del env # TODO(b/154635313): resolve the API mismatch + return False + + +class LocomotionGymEnvTest(tf.test.TestCase, parameterized.TestCase): + + def setUp(self): + super().setUp() + gin.clear_config() + + def test_env_from_gin(self): + # TODO(sehoonha) rename locomotion_gym_*test.gin to locomotion_gym_*.gin + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv() + self.assertIsInstance(env.robot, minitaur_v2.Minitaur) + # The robot will stand on the ground. + self.assertNear(env.robot.base_position[2], 0.25, 5e-2) + + def test_reset_gym(self): + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv(task=None) + + desired_init_motor_angle = math.pi / 2 + action_dim = len(env.action_space.high) + observations = env.reset(initial_motor_angles=[desired_init_motor_angle] * + action_dim) + observations = env_utils.flatten_observations(observations) + self.assertEqual(observations.size, 12) + self.assertNear(observations[0], 0, 1e-2) + self.assertNear(observations[4], desired_init_motor_angle, 2e-1) + + def test_step_gym(self): + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv(task=TestTask()) + + desired_motor_angle = math.pi / 3 + steps = 1000 + action_dim = len(env.action_space.high) + for _ in range(steps): + observations, reward, done, _ = env.step([desired_motor_angle] * + action_dim) + observations = env_utils.flatten_observations(observations) + + self.assertFalse(done) + self.assertEqual(reward, steps) + self.assertEqual(observations.size, 12) + self.assertNear(observations[0], 0, 1e-2) + self.assertNear(observations[4], desired_motor_angle, 2e-1) + np.testing.assert_allclose(env._last_action, + [desired_motor_angle] * action_dim, 2e-1) + + def test_scene(self): + gin.parse_config_file(_CONFIG_FILE) + data_root = 'urdf/' + env = locomotion_gym_env.LocomotionGymEnv( + task=TestTask(), scene=simple_scene.SimpleScene(data_root=data_root)) + desired_motor_angle = math.pi / 3 + steps = 2 + action_dim = len(env.action_space.high) + for _ in range(steps): + _, reward, _, _ = env.step([desired_motor_angle] * action_dim) + self.assertEqual(reward, steps) + + def test_except_on_invalid_config(self): + gin.parse_config_file(_CONFIG_FILE) + gin.bind_parameter('SimulationParameters.num_action_repeat', 0) + with self.assertRaises(ValueError): + locomotion_gym_env.LocomotionGymEnv(task=None) + + def test_no_scene(self): + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv(task=None, scene=None) + + # The robot will free fall. + self.assertNear(env.robot.base_position[2], 0.15, 5e-2) + + def test_seed_draw_with_np(self): + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv(task=None) + # first draw + env.seed(42) + nums1 = [] + for _ in range(3): + nums1.append(env.np_random.randint(2**31 - 1)) + # second draw + env.seed(42) + nums2 = [] + for _ in range(3): + nums2.append(env.np_random.randint(2**31 - 1)) + self.assertListEqual(nums1, nums2) + + def test_get_observations(self): + gin.parse_config_file(_CONFIG_FILE) + env = locomotion_gym_env.LocomotionGymEnv(task=None) + desired_init_motor_angle = math.pi / 2 + action_dim = len(env.action_space.high) + observations = env.reset(initial_motor_angles=[desired_init_motor_angle] * + action_dim) + self.assertLen(observations, 2) + self.assertLen(observations['IMU'], 4) + self.assertNear(observations['IMU'][0], 0, 1e-2) + self.assertLen(observations['MotorAngle'], 8) + self.assertNear(observations['MotorAngle'][0], desired_init_motor_angle, + 2e-1) + + + + + + def test_step_with_dynamic_objects(self): + gin.parse_config_file(_CONFIG_FILE_NEW_ROBOT) + + gin.parse_config([ + 'AutonomousObject.urdf_file = "urdf/mug.urdf"', + 'SceneBase.dynamic_objects = [@AutonomousObject(), @AutonomousObject()]', + 'LocomotionGymEnv.scene = @SceneBase()', + ]) + env = locomotion_gym_env.LocomotionGymEnv() + + self.assertLen(env.scene.dynamic_objects, 2) + for obj in env.scene.dynamic_objects: + self.assertIsInstance(obj, autonomous_object.AutonomousObject) + + # Replace dynamic objects with mocks for step tests. + mock_objects = [ + mock.create_autospec(autonomous_object.AutonomousObject), + mock.create_autospec(autonomous_object.AutonomousObject) + ] + env.scene._type_to_objects_dict[ + scene_base.ObjectType.DYNAMIC_OBJECT] = mock_objects + env.step(env.robot.action_space.sample()) + + expected_update_calls = [ + mock.call(i * env.sim_time_step, mock.ANY) + for i in range(env.num_action_repeat) + ] + expected_apply_action_calls = [ + mock.call(autonomous_object.AUTONOMOUS_ACTION) + for i in range(env.num_action_repeat) + ] + expected_receive_observation_calls = [ + mock.call() for i in range(env.num_action_repeat) + ] + + for mock_obj in mock_objects: + mock_obj.pre_control_step.assert_called_once_with( + autonomous_object.AUTONOMOUS_ACTION) + self.assertEqual(mock_obj.update.call_args_list, expected_update_calls) + self.assertEqual(mock_obj.apply_action.call_args_list, + expected_apply_action_calls) + self.assertEqual(mock_obj.receive_observation.call_args_list, + expected_receive_observation_calls) + mock_obj.post_control_step.assert_called_once_with() + + + def test_env_metrics(self): + gin.parse_config_file(_CONFIG_FILE_NEW_ROBOT) + env = locomotion_gym_env.LocomotionGymEnv() + metric_logger = env.metric_logger + test_metric_1 = metric_logger.create_scalar_metric( + 'test_metric_1', + scope=metric_lib.MetricScope.DEBUG, + single_ep_aggregator=np.mean) + test_metric_1.report(22) + + test_metric_2 = metric_logger.create_scalar_metric( + 'test_metric_2', + scope=metric_lib.MetricScope.PERFORMANCE, + single_ep_aggregator=np.max) + test_metric_2.report(15) + test_metric_2.report(16) + + all_metrics = metric_logger.get_episode_metrics() + + self.assertDictEqual(all_metrics, { + 'DEBUG/test_metric_1': 22, + 'PERFORMANCE/test_metric_2': 16 + }) + + env.reset() + + all_metrics = metric_logger.get_episode_metrics() + self.assertDictEqual(all_metrics, {}) + + +if __name__ == '__main__': + tf.test.main() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/multiagent_mobility_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/multiagent_mobility_gym_env.py new file mode 100644 index 000000000..c716d367b --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/multiagent_mobility_gym_env.py @@ -0,0 +1,485 @@ +"""This file implements the locomotion gym env.""" +# pylint: disable=dangerous-default-value + +import atexit +import collections +import time + +import gin +from gym import spaces +import numpy as np + +from pybullet_utils import bullet_client +from pybullet_envs.minitaur.envs import minitaur_logging +from pybullet_envs.minitaur.envs import minitaur_logging_pb2 +from pybullet_envs.minitaur.envs_v2 import locomotion_gym_env +from pybullet_envs.minitaur.envs_v2.scenes import scene_base +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.sensors import space_utils +import pybullet + +_ACTION_EPS = 0.01 +_NUM_SIMULATION_ITERATION_STEPS = 300 +_LOG_BUFFER_LENGTH = 5000 + + +@gin.configurable +class MultiagentMobilityGymEnv(locomotion_gym_env.LocomotionGymEnv): + """The gym environment for the locomotion tasks.""" + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 100 + } + + def __init__(self, + gym_config, + robot_classes, + scene: scene_base.SceneBase = scene_base.SceneBase(), + sensors=None, + tasks=[], + global_task=None, + single_reward=False, + env_randomizers=None): + """Initializes the locomotion gym environment. + + Args: + gym_config: An instance of LocomotionGymConfig. + robot_classes: A list of robot classes. We provide a class rather than an + instance due to hard_reset functionality. Parameters are expected to be + configured with gin. + scene: An object for managing the robot's surroundings. + sensors: A list of environmental sensors for observation. This does not + include on-robot sensors. + tasks: A list of callable function/class to calculate the reward and + termination condition. Takes the gym env as the argument when calling. + global_task: A callable function/class to calculate the reward and + termination condition for all robots. Takes the gym env as the argument + when calling. + single_reward: Whether the environment returns a single reward for all + agents or a dictionary. + env_randomizers: A list of EnvRandomizer(s). An EnvRandomizer may + randomize the physical property of minitaur, change the terrrain during + reset(), or add perturbation forces during step(). + + Raises: + ValueError: If the num_action_repeat is less than 1, or if number of + unique robot names do not match the number of robot classes. + + """ + # TODO(sehoonha) split observation and full-state sensors (b/129858214) + + # Makes sure that close() is always called to flush out the logs to the + # disk. + atexit.register(self.close) + self.seed() + self._gym_config = gym_config + self._robot_classes = robot_classes + # Checking uniqueness of names and number of names + self._scene = scene + # TODO(sehoonha) change the data structure to dictionary + # TODO(b/144521291) make sure sensors have their own robot names + self._sensors = sensors if sensors is not None else list() + self._log_path = gym_config.log_path + self._logging = minitaur_logging.MinitaurLogging(self._log_path) + self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() + self._data_dir = gym_config.data_dir + + # A dictionary containing the objects in the world other than the robot. + self._tasks = tasks + self._global_task = global_task + self._single_reward = single_reward + + self._env_randomizers = env_randomizers if env_randomizers else [] + + # This is a workaround due to the issue in b/130128505#comment5 + for task in self._tasks: + if isinstance(task, sensor.Sensor): + self._sensors.append(task) + if global_task and isinstance(global_task, sensor.Sensor): + self._sensors.append(global_task) + + # Simulation related parameters. + self._num_action_repeat = gym_config.simulation_parameters.num_action_repeat + self._on_rack = gym_config.simulation_parameters.robot_on_rack + if self._num_action_repeat < 1: + raise ValueError('number of action repeats should be at least 1.') + self._sim_time_step = gym_config.simulation_parameters.sim_time_step_s + self._env_time_step = self._num_action_repeat * self._sim_time_step + self._env_step_counter = 0 + + # TODO(b/73829334): Fix the value of self._num_bullet_solver_iterations. + self._num_bullet_solver_iterations = int(_NUM_SIMULATION_ITERATION_STEPS / + self._num_action_repeat) + self._is_render = gym_config.simulation_parameters.enable_rendering + + # The wall-clock time at which the last frame is rendered. + self._last_frame_time = 0.0 + if self._is_render: + self._pybullet_client = bullet_client.BulletClient( + connection_mode=pybullet.GUI) + else: + self._pybullet_client = bullet_client.BulletClient() + + if gym_config.simulation_parameters.egl_rendering: + self._pybullet_client.loadPlugin('eglRendererPlugin') + self._pybullet_client.enable_cns() + + # If enabled, save the performance profile to profiling_path + # Use Google Chrome about://tracing to open the file + if gym_config.profiling_path is not None: + self._profiling_slot = self._pybullet_client.startStateLogging( + self._pybullet_client.STATE_LOGGING_PROFILE_TIMINGS, + gym_config.profiling_path) + self._profiling_counter = 10 + else: + self._profiling_slot = -1 + # Build the action space. The action space must be compatible with the + # robot configuration. + + # The action list contains the name of all actions. + # TODO(b/144479707): Allow robots to set the action space automatically. + + action_space = collections.OrderedDict() + for robot_name, action in gym_config.actions.items(): + action_lower_bound = [] + action_upper_bound = [] + for action_scalar in action: + action_upper_bound.append(action_scalar.upper_bound) + action_lower_bound.append(action_scalar.lower_bound) + action_space[robot_name] = spaces.Box( + np.asarray(action_lower_bound), + np.asarray(action_upper_bound), + dtype=np.float32) + self.action_space = spaces.Dict(action_space) + + # Set the default render options. + self._camera_dist = gym_config.simulation_parameters.camera_distance + self._camera_yaw = gym_config.simulation_parameters.camera_yaw + self._camera_pitch = gym_config.simulation_parameters.camera_pitch + self._render_width = gym_config.simulation_parameters.render_width + self._render_height = gym_config.simulation_parameters.render_height + + self._hard_reset = True + self.reset() + + self._hard_reset = gym_config.simulation_parameters.enable_hard_reset + + # Construct the observation space from the list of sensors. Note that we + # will reconstruct the observation_space after the robot is created. + self.observation_space = ( + space_utils.convert_sensors_to_gym_space_dictionary(self.all_sensors())) + + def close(self): + if self._log_path is not None: + self._logging.save_episode(self._episode_proto) + + for robot in self._robots: + robot.Terminate() + + def all_sensors(self): + """Returns all robot and environmental sensors.""" + robot_sensors = [] + for robot in self._robots: + robot_sensors += robot.GetAllSensors() + return robot_sensors + self._sensors + + @gin.configurable('multiagent_mobility_gym_env.MultiagentMobilityGymEnv.reset' + ) + def reset(self, + initial_motor_angles=None, + reset_duration=1.0, + reset_visualization_camera=True): + """Resets the robot's position in the world or rebuild the sim world. + + The simulation world will be rebuilt if self._hard_reset is True. + + Args: + initial_motor_angles: A list of Floats. The desired joint angles after + reset. If None, the robot will use its built-in value. + reset_duration: Float. The time (in seconds) needed to rotate all motors + to the desired initial values. + reset_visualization_camera: Whether to reset debug visualization camera on + reset. + + Returns: + A numpy array contains the initial observation after reset. + """ + if self._is_render: + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_RENDERING, 0) + + # Clear the simulation world and rebuild the robot interface. + if self._hard_reset: + self._pybullet_client.resetSimulation() + self._pybullet_client.setPhysicsEngineParameter( + numSolverIterations=self._num_bullet_solver_iterations) + self._pybullet_client.setTimeStep(self._sim_time_step) + self._pybullet_client.setGravity(0, 0, -10) + + # Rebuild the world. + self._scene.build_scene(self._pybullet_client) + + # Rebuild the robots + # TODO(b/144545080): Make this scale to more than two agents + # Have multiple robot classes as a list. + self._robots = [] + for robot_class in self._robot_classes: + + self._robots.append( + robot_class( + pybullet_client=self._pybullet_client, + # TODO(rosewang): Remove on rack in multiagent acase + on_rack=self._on_rack)) + + # Reset the pose of the robot. + for robot in self._robots: + robot.Reset( + reload_urdf=False, + default_motor_angles=initial_motor_angles, + reset_time=reset_duration) + + self._env_step_counter = 0 + self._pybullet_client.resetDebugVisualizerCamera(self._camera_dist, + self._camera_yaw, + self._camera_pitch, + [0, 0, 0]) + + # Flush the logs to disc and reinitialize the logging system. + if self._log_path is not None: + self._logging.save_episode(self._episode_proto) + self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() + minitaur_logging.preallocate_episode_proto(self._episode_proto, + _LOG_BUFFER_LENGTH, + self._robots[0]) + self._pybullet_client.setPhysicsEngineParameter(enableConeFriction=0) + self._env_step_counter = 0 + if reset_visualization_camera: + self._pybullet_client.resetDebugVisualizerCamera(self._camera_dist, + self._camera_yaw, + self._camera_pitch, + [0, 0, 0]) + + self._last_action = { + robot_name: np.zeros(space.shape) + for robot_name, space in self.action_space.spaces.items() + } + + if self._is_render: + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_RENDERING, 1) + + for s in self.all_sensors(): + # set name + if any([r.name in s.get_name() for r in self.robots]): + robot = [r for r in self.robots if r.name in s.get_name()][0] + s.set_robot(robot) + + for task in self._tasks: + if hasattr(task, 'reset'): + task.reset(self) + if self._global_task and hasattr(self._global_task, 'reset'): + self._global_task.reset(self) + + # Loop over all env randomizers. + for env_randomizer in self._env_randomizers: + env_randomizer.randomize_env(self) + + for s in self.all_sensors(): + s.on_reset(self) + + return self._get_observation() + + def get_robot(self, name): + for robot in self.robots: + if robot.name == name: + return robot + + def _reward(self): + """Returns a list of rewards. + + Returns: + A list of rewards corresponding to each robot and their task. + """ + global_reward = 0 + if self._global_task: + global_reward = self._global_task(self) + if self._single_reward: # Needed for tfagents compatibility. + if self._tasks: + return min([task(self) + global_reward for task in self._tasks]) + return 0 + else: + if self._tasks: + return [task(self) + global_reward for task in self._tasks] + return [0 for _ in self.robots] + + def step(self, actions): + """Step forward the simulation, given the actions. + + Args: + actions: A dictionary of actions for all robots. The action for each robot + can be joint angles for legged platforms like Laikago, and base + velocity/steering for kinematic robots such like Fetch. + + Returns: + observations: The observation dictionary. The keys are the sensor names + and the values are the sensor readings. + reward: The reward for the current state-action pair. + done: Whether the episode has ended. + info: A dictionary that stores diagnostic information. + + Raises: + ValueError: The action dimension is not the same as the number of motors. + ValueError: The magnitude of actions is out of bounds. + """ + self._last_base_position = [ + robot.GetBasePosition() for robot in self._robots + ] + self._last_action = actions + + if self._is_render: + # Sleep, otherwise the computation takes less time than real time, + # which will make the visualization like a fast-forward video. + time_spent = time.time() - self._last_frame_time + self._last_frame_time = time.time() + time_to_sleep = self._env_time_step - time_spent + if time_to_sleep > 0: + time.sleep(time_to_sleep) + camera_target = np.mean( + [robot.GetBasePosition() for robot in self._robots], axis=0) + + # Also keep the previous orientation of the camera set by the user. + [yaw, pitch, + dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] + self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, + camera_target) + + for env_randomizer in self._env_randomizers: + env_randomizer.randomize_step(self) + + # Stepping broken down into their parts + for robot in self._robots: + robot.PreStepPerStep(actions) + + for _ in range(self._num_action_repeat): + for robot in self._robots: + robot.PreStepPerActionRepeat(actions) + + self._pybullet_client.stepSimulation() + + for robot in self._robots: + robot.PostStepPerActionRepeat() + + for robot in self._robots: + robot.PostStepPerStep() + + if self._profiling_slot >= 0: + self._profiling_counter -= 1 + if self._profiling_counter == 0: + self._pybullet_client.stopStateLogging(self._profiling_slot) + if self._log_path is not None: + minitaur_logging.update_episode_proto(self._episode_proto, + self._robots[0], actions, + self._env_step_counter) + reward = self._reward() + + for s in self.all_sensors(): + s.on_step(self) + + for task in self._tasks: + if hasattr(task, 'update'): + task.update(self) + if self._global_task and hasattr(self._global_task, 'update'): + self._global_task.update(self) + + done = self._termination() + self._env_step_counter += 1 + if done: + for robot in self._robots: + robot.Terminate() + return self._get_observation(), reward, done, {} + + def render(self, mode='rgb_array'): + if mode != 'rgb_array': + raise ValueError('Unsupported render mode:{}'.format(mode)) + base_pos = np.mean([robot.GetBasePosition() for robot in self._robots], + axis=0) + view_matrix = self._pybullet_client.computeViewMatrixFromYawPitchRoll( + cameraTargetPosition=base_pos, + distance=self._camera_dist, + yaw=self._camera_yaw, + pitch=self._camera_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( + fov=60, + aspect=float(self._render_width) / self._render_height, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._pybullet_client.getCameraImage( + width=self._render_width, + height=self._render_height, + renderer=self._pybullet_client.ER_BULLET_HARDWARE_OPENGL, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix) + rgb_array = np.array(px) + rgb_array = rgb_array[:, :, :3] + return rgb_array + + def _termination(self): + if not all([robot.is_safe for robot in self._robots]): + return True + + if self._tasks: + return (self._global_task and self._global_task.done(self)) or any( + [task.done(self) for task in self._tasks]) + + for s in self.all_sensors(): + s.on_terminate(self) + + return False + + def set_time_step(self, num_action_repeat, sim_step=0.001): + """Sets the time step of the environment. + + Args: + num_action_repeat: The number of simulation steps/action repeats to be + executed when calling env.step(). + sim_step: The simulation time step in PyBullet. By default, the simulation + step is 0.001s, which is a good trade-off between simulation speed and + accuracy. + + Raises: + ValueError: If the num_action_repeat is less than 1. + """ + if num_action_repeat < 1: + raise ValueError('number of action repeats should be at least 1.') + self._sim_time_step = sim_step + self._num_action_repeat = num_action_repeat + self._env_time_step = sim_step * num_action_repeat + self._num_bullet_solver_iterations = ( + _NUM_SIMULATION_ITERATION_STEPS / self._num_action_repeat) + self._pybullet_client.setPhysicsEngineParameter( + numSolverIterations=self._num_bullet_solver_iterations) + self._pybullet_client.setTimeStep(self._sim_time_step) + for robot in self._robots: + robot.SetTimeSteps(self._num_action_repeat, self._sim_time_step) + + def get_time_since_reset(self): + """Get the time passed (in seconds) since the last reset. + + Returns: + List of time in seconds since the last reset for each robot. + """ + return self._robots[0].GetTimeSinceReset() + + @property + def tasks(self): + return self._tasks + + @property + def robots(self): + return self._robots + + @property + def num_robots(self): + return len(self._robots) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/scene_base.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/scene_base.py new file mode 100644 index 000000000..c52ce10f6 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/scene_base.py @@ -0,0 +1,256 @@ +# Lint as: python3 +"""Class for loading and managing a scene in pybullet.""" + +import enum +from typing import Any, Dict, List, Optional, Sequence, Text +import gin +import numpy as np + +from pybullet_envs.minitaur.envs_v2.scenes import world_asset_pb2 +from pybullet_envs.minitaur.envs_v2 import base_client +from pybullet_utils import bullet_client +from pybullet_envs.minitaur.robots import autonomous_object + + + +# The 2D coordinates of the corners of a polygon. The corners are specified in +# counterclock-wise direction. +Polygon = Sequence[Sequence[float]] + + +class ObjectType(enum.Enum): + """Categories of objects that may be found in a scene.""" + OTHER = 0 + GROUND = 1 + OBSTACLE = 2 + GOAL = 3 + DYNAMIC_OBJECT = 4 + + +@gin.configurable +class SceneBase(object): + """Class for loading and managing a scene.""" + + def __init__( + self, + data_root: Text = None, + dynamic_objects: Sequence[autonomous_object.AutonomousObject] = ()): + """Initializes SceneBase. + + Args: + data_root: Root directory for finding object models. + dynamic_objects: Dynamic objects to be added into the scene. + crowd_builders: Builders of crowds formed by autonomous objects. + """ + self._pybullet_client = None + self._data_root = data_root + temp_dynamic_objects = list(dynamic_objects) + + self._dynamic_objects = tuple(temp_dynamic_objects) + # Dictionaries and world_asset are declared outside init to make sure they + # are all reset in _reset_scene_tracking(). + self._reset_scene_tracking() + + def _reset_scene_tracking(self): + """Clears all scene dicts. Used when the simulation is reset.""" + self._type_to_ids_dict = {object_type: [] for object_type in ObjectType} + self._type_to_objects_dict = {object_type: [] for object_type in ObjectType} + self._id_to_type_dict = {} + self._id_to_object_dict = {} + self._world_asset = None + + def destroy_scene(self): + """Destroys contents of scene to get ready for another build_scene call.""" + id_to_remove = list(self._id_to_object_dict.keys()) + for object_id in id_to_remove: + self.remove_object(object_id) + self._reset_scene_tracking() + + def build_scene(self, pybullet_client: bullet_client.BulletClient): + """Loads and positions all scene objects in pybullet. + + Override this function in subclass to implement customized scene. The + overriding function must call base function first. + + Args: + pybullet_client: A pybullet client in which the scene will be built. + """ + self._reset_scene_tracking() + self._pybullet_client = pybullet_client + self._init_dynamic_objects() + + def reset(self): + """The soft reset of scene. + + Unlike "build_scene", this is called at each env.reset() before robot + resetting. Typically we use this API to do some soft resetting like putting + objects back to its place. Howevever, for special cases such as P2P multimap + training, we can reload a different mesh scene once a while. + """ + pass + + def _init_dynamic_objects(self): + """Adds dynamic objects to scene.""" + for an_object in self._dynamic_objects: + an_object.set_sim_client(self._pybullet_client) + self.add_object(an_object.sim_object_id, ObjectType.DYNAMIC_OBJECT, + an_object) + + @property + def pybullet_client(self) -> bullet_client.BulletClient: + if self._pybullet_client is None: + raise ValueError("pybullet_client is None; did you call build_scene()?") + return self._pybullet_client + + @property + def ground_height(self) -> float: + """Returns ground height of the scene.""" + return 0.0 + + @property + def ground_ids(self) -> List[int]: + """Returns the pybullet ids of the ground.""" + return self._type_to_ids_dict[ObjectType.GROUND] + + @property + def obstacle_ids(self) -> List[int]: + """Returns the pybullet ids of all obstacles in the scene.""" + return self._type_to_ids_dict[ObjectType.OBSTACLE] + + @property + def goal_ids(self) -> List[int]: + """Returns the pybullet ids of any goals in the scene.""" + return self._type_to_ids_dict[ObjectType.GOAL] + + @property + def dynamic_object_ids(self) -> List[int]: + """Returns the pybullet ids of dynamic objects.""" + return self._type_to_ids_dict[ObjectType.DYNAMIC_OBJECT] + + @property + def dynamic_objects(self) -> List[autonomous_object.AutonomousObject]: + """Returns the dynamic objects python object (AutonomousObject).""" + return self._type_to_objects_dict[ObjectType.DYNAMIC_OBJECT] + + @property + def world_asset(self) -> world_asset_pb2.WorldAsset: + """Returns a proto describing the semantics of the scene. + + If the scene keeps a WorldAsset, then mutating this proto will mutate it for + everyone. If the scene generates a WorldAsset from _type_to_ids_dict, then + this is not an issue. + """ + if self._world_asset: + return self._world_asset + return self._dict_to_world_asset(self._type_to_ids_dict) + + def add_object(self, + object_id: int, + class_label: ObjectType, + python_object: Optional[Any] = None): + """Adds an object to be tracked. + + Does not load anything into pybullet. + + Args: + object_id: objectUniqueId from pybullet. + class_label: What type to consider the new object. + python_object: Associated python object for the pybullet object of + objectUniqueId == object_id. Environment uses the python object to + control object in pybullet in these cases. One example is python objects + of class label DYNAMIC_OBJECT: they are associated with python objects + of type AutonomousObject. + """ + if python_object is not None: + if (isinstance(python_object, autonomous_object.AutonomousObject) and + python_object.sim_object_id != object_id): + raise ValueError( + f"Mismatch object ids, object_id = {object_id}, sim_object_id = " + f"{python_object.sim_object_id}") + self._type_to_objects_dict[class_label].append(python_object) + self._type_to_ids_dict[class_label].append(object_id) + self._id_to_type_dict[object_id] = class_label + self._id_to_object_dict[object_id] = python_object + + def remove_object(self, object_id: int): + """Removes an object from tracking and from pybullet. + + Args: + object_id: objectUniqueID from pybullet. + + Raises: + KeyError: if object_id does not exist in the record. + """ + if object_id not in self._id_to_type_dict.keys(): + raise KeyError( + f"Object with object_id = {object_id} does not exist in the record.") + + self.pybullet_client.removeBody(object_id) + object_type = self._id_to_type_dict[object_id] + self._type_to_ids_dict[object_type].remove(object_id) + + object_to_remove = self.id_to_object(object_id) + if object_to_remove is not None: + # Removes item by identity comparison and avoid slow down due to objects + # with complex equality comparison function. list.remove() compares + # equality instead of identity. + for i, an_object in enumerate(self._type_to_objects_dict[object_type]): + if an_object is object_to_remove: + del self._type_to_objects_dict[object_type][i] + break + del self._id_to_type_dict[object_id] + del self._id_to_object_dict[object_id] + + def id_to_object(self, object_id: int) -> Any: + """Returns underlying python object from sim object id. + + Args: + object_id: objectUniqueID from pybullet. + + Returns: + None is returned if the sim object does not have a corresponding python + object. + """ + return self._id_to_object_dict[object_id] + + def _dict_to_world_asset( + self, type_to_ids_dict: Dict[ObjectType, + List[int]]) -> world_asset_pb2.WorldAsset: + """Converts a dictionary to a WorldAsset. + + Args: + type_to_ids_dict: Dictionary that describes the scene. Keys are + ObjectTypes and values are lists of integers, where each integer is a + pybullet id for an object of a given type. + + Returns: + A WorldAsset proto with the types, locations and bounding boxes of all + objects in the scene. + """ + world_asset = world_asset_pb2.WorldAsset() + for object_type in type_to_ids_dict.keys(): + for obj_id in type_to_ids_dict[object_type]: + bbox = np.array(self.pybullet_client.getAABB(obj_id)) + bbox_center = np.mean(bbox, axis=0) + bbox_dimensions = bbox[1] - bbox[0] + + obj = world_asset_pb2.Object() + obj.id = str(obj_id) + obj.label = str(object_type) + obj.bounding_box.center.x = bbox_center[0] + obj.bounding_box.center.y = bbox_center[1] + obj.bounding_box.center.z = bbox_center[2] + obj.bounding_box.dimensions.x = bbox_dimensions[0] + obj.bounding_box.dimensions.y = bbox_dimensions[1] + obj.bounding_box.dimensions.z = bbox_dimensions[2] + world_asset.objects.append(obj) + return world_asset + + def close(self): + """Closes the scene at the end of life cycle of the environment.""" + pass + + @property + def vectorized_map(self) -> Sequence[Polygon]: + """Returns vectorized map containing a list of polygon obstacles.""" + raise NotImplementedError("vectorized_map is not implemented by default.") diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/simple_scene.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/simple_scene.py new file mode 100644 index 000000000..0099e9e96 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/simple_scene.py @@ -0,0 +1,35 @@ +# Lint as: python3 +"""A scene containing only a planar floor.""" + +from typing import Sequence + +import gin +from pybullet_envs.minitaur.envs_v2 import base_client +from pybullet_envs.minitaur.envs_v2.scenes import scene_base + +_PLANE_URDF = ( + "plane.urdf") + + +@gin.configurable +class SimpleScene(scene_base.SceneBase): + """A scene containing only a planar floor.""" + + def build_scene(self, pybullet_client): + super().build_scene(pybullet_client) + + visual_shape_id = self._pybullet_client.createVisualShape( + shapeType=self._pybullet_client.GEOM_PLANE) + collision_shape_id = self._pybullet_client.createCollisionShape( + shapeType=self._pybullet_client.GEOM_PLANE) + ground_id = self._pybullet_client.createMultiBody( + baseMass=0, + baseCollisionShapeIndex=collision_shape_id, + baseVisualShapeIndex=visual_shape_id) + self._pybullet_client.changeDynamics(ground_id, -1, lateralFriction=1.0) + self.add_object(ground_id, scene_base.ObjectType.GROUND) + + @property + def vectorized_map(self) -> Sequence[scene_base.Polygon]: + """Returns vectorized map containing a list of polygon obstacles.""" + return [] diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset.proto b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset.proto new file mode 100644 index 000000000..0e0b97cc4 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset.proto @@ -0,0 +1,86 @@ +// A set of protocol buffer definitions for representing 'Worlds' for +// used by simulation engines. +syntax = "proto3"; + +package robotics.messages; +option cc_enable_arenas = true; + + +// A single precision quaternion. +message QQuaternionf { + // The x-component. + float x = 1; + // The y-component. + float y = 2; + // The z-component. + float z = 3; + // The w-component. + float w = 4; +} + + + + +// A three-dimensional single precision vector. +message VVector3f { + float x = 1; + float y = 2; + float z = 3; +} + + + +message Labels { + map label = 1; +} + +// A general bounding box is specified by its center, dimensions and +// orientation. If the orientation field is not specified, then the bounding box +// is aligned with the axes of the world coordinate system. +message GeneralBox3f { + VVector3f center = 1; + VVector3f dimensions = 2; + QQuaternionf orientation = 3; +} + +// An object is specified by its label, bounding box, and a path to an obj +// file containing its mesh. Not all to be specified. +message Object { + string id = 1; + string label = 2; + GeneralBox3f bounding_box = 3; + string mesh_path = 4; +} + +message Polygon3f { + repeated VVector3f vertex = 1; +} + +// A region is specified by its label, spatial extent, and a path to an obj +// file containing its mesh. Not all to be specified. +message Region { + string id = 1; + string label = 2; + oneof spatial_extent { + GeneralBox3f bounding_box = 3; + Polygon3f polygon = 5; + } + string mesh_path = 6; +} + +// A world consist of a mesh, and potentially a set of object and regions. +message WorldAsset { + // A set of labels for objects. This the space of all object labels. + Labels object_labels = 1; + repeated Object objects = 2; + + // A set of labels for regions. This the space of all region labels. + Labels region_labels = 3; + repeated Region regions = 4; + + // A path to a mesh (as obj file) containing the geometry of the world. + string mesh_path = 5; + + // A path to a mesh (as obj file) containing the semantic labels of the world. + string segmentation_mesh_path = 6; +} diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset_pb2.py new file mode 100644 index 000000000..a7d99baea --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/world_asset_pb2.py @@ -0,0 +1,555 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: world_asset.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor.FileDescriptor( + name='world_asset.proto', + package='robotics.messages', + syntax='proto3', + serialized_options=b'\370\001\001', + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\x11world_asset.proto\x12\x11robotics.messages\":\n\x0cQQuaternionf\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\",\n\tVVector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\"k\n\x06Labels\x12\x33\n\x05label\x18\x01 \x03(\x0b\x32$.robotics.messages.Labels.LabelEntry\x1a,\n\nLabelEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\xa4\x01\n\x0cGeneralBox3f\x12,\n\x06\x63\x65nter\x18\x01 \x01(\x0b\x32\x1c.robotics.messages.VVector3f\x12\x30\n\ndimensions\x18\x02 \x01(\x0b\x32\x1c.robotics.messages.VVector3f\x12\x34\n\x0borientation\x18\x03 \x01(\x0b\x32\x1f.robotics.messages.QQuaternionf\"m\n\x06Object\x12\n\n\x02id\x18\x01 \x01(\t\x12\r\n\x05label\x18\x02 \x01(\t\x12\x35\n\x0c\x62ounding_box\x18\x03 \x01(\x0b\x32\x1f.robotics.messages.GeneralBox3f\x12\x11\n\tmesh_path\x18\x04 \x01(\t\"9\n\tPolygon3f\x12,\n\x06vertex\x18\x01 \x03(\x0b\x32\x1c.robotics.messages.VVector3f\"\xb2\x01\n\x06Region\x12\n\n\x02id\x18\x01 \x01(\t\x12\r\n\x05label\x18\x02 \x01(\t\x12\x37\n\x0c\x62ounding_box\x18\x03 \x01(\x0b\x32\x1f.robotics.messages.GeneralBox3fH\x00\x12/\n\x07polygon\x18\x05 \x01(\x0b\x32\x1c.robotics.messages.Polygon3fH\x00\x12\x11\n\tmesh_path\x18\x06 \x01(\tB\x10\n\x0espatial_extent\"\xfb\x01\n\nWorldAsset\x12\x30\n\robject_labels\x18\x01 \x01(\x0b\x32\x19.robotics.messages.Labels\x12*\n\x07objects\x18\x02 \x03(\x0b\x32\x19.robotics.messages.Object\x12\x30\n\rregion_labels\x18\x03 \x01(\x0b\x32\x19.robotics.messages.Labels\x12*\n\x07regions\x18\x04 \x03(\x0b\x32\x19.robotics.messages.Region\x12\x11\n\tmesh_path\x18\x05 \x01(\t\x12\x1e\n\x16segmentation_mesh_path\x18\x06 \x01(\tB\x03\xf8\x01\x01\x62\x06proto3' +) + + + + +_QQUATERNIONF = _descriptor.Descriptor( + name='QQuaternionf', + full_name='robotics.messages.QQuaternionf', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.QQuaternionf.x', index=0, + number=1, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.QQuaternionf.y', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.QQuaternionf.z', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='w', full_name='robotics.messages.QQuaternionf.w', index=3, + number=4, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=40, + serialized_end=98, +) + + +_VVECTOR3F = _descriptor.Descriptor( + name='VVector3f', + full_name='robotics.messages.VVector3f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.VVector3f.x', index=0, + number=1, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.VVector3f.y', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.VVector3f.z', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=100, + serialized_end=144, +) + + +_LABELS_LABELENTRY = _descriptor.Descriptor( + name='LabelEntry', + full_name='robotics.messages.Labels.LabelEntry', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='key', full_name='robotics.messages.Labels.LabelEntry.key', index=0, + number=1, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='value', full_name='robotics.messages.Labels.LabelEntry.value', index=1, + number=2, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=b'8\001', + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=209, + serialized_end=253, +) + +_LABELS = _descriptor.Descriptor( + name='Labels', + full_name='robotics.messages.Labels', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='label', full_name='robotics.messages.Labels.label', index=0, + number=1, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[_LABELS_LABELENTRY, ], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=146, + serialized_end=253, +) + + +_GENERALBOX3F = _descriptor.Descriptor( + name='GeneralBox3f', + full_name='robotics.messages.GeneralBox3f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='center', full_name='robotics.messages.GeneralBox3f.center', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='dimensions', full_name='robotics.messages.GeneralBox3f.dimensions', index=1, + number=2, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='orientation', full_name='robotics.messages.GeneralBox3f.orientation', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=256, + serialized_end=420, +) + + +_OBJECT = _descriptor.Descriptor( + name='Object', + full_name='robotics.messages.Object', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='id', full_name='robotics.messages.Object.id', index=0, + number=1, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='label', full_name='robotics.messages.Object.label', index=1, + number=2, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='bounding_box', full_name='robotics.messages.Object.bounding_box', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='mesh_path', full_name='robotics.messages.Object.mesh_path', index=3, + number=4, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=422, + serialized_end=531, +) + + +_POLYGON3F = _descriptor.Descriptor( + name='Polygon3f', + full_name='robotics.messages.Polygon3f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='vertex', full_name='robotics.messages.Polygon3f.vertex', index=0, + number=1, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=533, + serialized_end=590, +) + + +_REGION = _descriptor.Descriptor( + name='Region', + full_name='robotics.messages.Region', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='id', full_name='robotics.messages.Region.id', index=0, + number=1, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='label', full_name='robotics.messages.Region.label', index=1, + number=2, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='bounding_box', full_name='robotics.messages.Region.bounding_box', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='polygon', full_name='robotics.messages.Region.polygon', index=3, + number=5, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='mesh_path', full_name='robotics.messages.Region.mesh_path', index=4, + number=6, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + _descriptor.OneofDescriptor( + name='spatial_extent', full_name='robotics.messages.Region.spatial_extent', + index=0, containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[]), + ], + serialized_start=593, + serialized_end=771, +) + + +_WORLDASSET = _descriptor.Descriptor( + name='WorldAsset', + full_name='robotics.messages.WorldAsset', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='object_labels', full_name='robotics.messages.WorldAsset.object_labels', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='objects', full_name='robotics.messages.WorldAsset.objects', index=1, + number=2, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='region_labels', full_name='robotics.messages.WorldAsset.region_labels', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='regions', full_name='robotics.messages.WorldAsset.regions', index=3, + number=4, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='mesh_path', full_name='robotics.messages.WorldAsset.mesh_path', index=4, + number=5, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='segmentation_mesh_path', full_name='robotics.messages.WorldAsset.segmentation_mesh_path', index=5, + number=6, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=b"".decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=774, + serialized_end=1025, +) + +_LABELS_LABELENTRY.containing_type = _LABELS +_LABELS.fields_by_name['label'].message_type = _LABELS_LABELENTRY +_GENERALBOX3F.fields_by_name['center'].message_type = _VVECTOR3F +_GENERALBOX3F.fields_by_name['dimensions'].message_type = _VVECTOR3F +_GENERALBOX3F.fields_by_name['orientation'].message_type = _QQUATERNIONF +_OBJECT.fields_by_name['bounding_box'].message_type = _GENERALBOX3F +_POLYGON3F.fields_by_name['vertex'].message_type = _VVECTOR3F +_REGION.fields_by_name['bounding_box'].message_type = _GENERALBOX3F +_REGION.fields_by_name['polygon'].message_type = _POLYGON3F +_REGION.oneofs_by_name['spatial_extent'].fields.append( + _REGION.fields_by_name['bounding_box']) +_REGION.fields_by_name['bounding_box'].containing_oneof = _REGION.oneofs_by_name['spatial_extent'] +_REGION.oneofs_by_name['spatial_extent'].fields.append( + _REGION.fields_by_name['polygon']) +_REGION.fields_by_name['polygon'].containing_oneof = _REGION.oneofs_by_name['spatial_extent'] +_WORLDASSET.fields_by_name['object_labels'].message_type = _LABELS +_WORLDASSET.fields_by_name['objects'].message_type = _OBJECT +_WORLDASSET.fields_by_name['region_labels'].message_type = _LABELS +_WORLDASSET.fields_by_name['regions'].message_type = _REGION +DESCRIPTOR.message_types_by_name['QQuaternionf'] = _QQUATERNIONF +DESCRIPTOR.message_types_by_name['VVector3f'] = _VVECTOR3F +DESCRIPTOR.message_types_by_name['Labels'] = _LABELS +DESCRIPTOR.message_types_by_name['GeneralBox3f'] = _GENERALBOX3F +DESCRIPTOR.message_types_by_name['Object'] = _OBJECT +DESCRIPTOR.message_types_by_name['Polygon3f'] = _POLYGON3F +DESCRIPTOR.message_types_by_name['Region'] = _REGION +DESCRIPTOR.message_types_by_name['WorldAsset'] = _WORLDASSET +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +QQuaternionf = _reflection.GeneratedProtocolMessageType('QQuaternionf', (_message.Message,), { + 'DESCRIPTOR' : _QQUATERNIONF, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.QQuaternionf) + }) +_sym_db.RegisterMessage(QQuaternionf) + +VVector3f = _reflection.GeneratedProtocolMessageType('VVector3f', (_message.Message,), { + 'DESCRIPTOR' : _VVECTOR3F, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.VVector3f) + }) +_sym_db.RegisterMessage(VVector3f) + +Labels = _reflection.GeneratedProtocolMessageType('Labels', (_message.Message,), { + + 'LabelEntry' : _reflection.GeneratedProtocolMessageType('LabelEntry', (_message.Message,), { + 'DESCRIPTOR' : _LABELS_LABELENTRY, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Labels.LabelEntry) + }) + , + 'DESCRIPTOR' : _LABELS, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Labels) + }) +_sym_db.RegisterMessage(Labels) +_sym_db.RegisterMessage(Labels.LabelEntry) + +GeneralBox3f = _reflection.GeneratedProtocolMessageType('GeneralBox3f', (_message.Message,), { + 'DESCRIPTOR' : _GENERALBOX3F, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.GeneralBox3f) + }) +_sym_db.RegisterMessage(GeneralBox3f) + +Object = _reflection.GeneratedProtocolMessageType('Object', (_message.Message,), { + 'DESCRIPTOR' : _OBJECT, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Object) + }) +_sym_db.RegisterMessage(Object) + +Polygon3f = _reflection.GeneratedProtocolMessageType('Polygon3f', (_message.Message,), { + 'DESCRIPTOR' : _POLYGON3F, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Polygon3f) + }) +_sym_db.RegisterMessage(Polygon3f) + +Region = _reflection.GeneratedProtocolMessageType('Region', (_message.Message,), { + 'DESCRIPTOR' : _REGION, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Region) + }) +_sym_db.RegisterMessage(Region) + +WorldAsset = _reflection.GeneratedProtocolMessageType('WorldAsset', (_message.Message,), { + 'DESCRIPTOR' : _WORLDASSET, + '__module__' : 'world_asset_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.WorldAsset) + }) +_sym_db.RegisterMessage(WorldAsset) + + +DESCRIPTOR._options = None +_LABELS_LABELENTRY._options = None +# @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/accelerometer_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/accelerometer_sensor.py new file mode 100644 index 000000000..954e8ecb3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/accelerometer_sensor.py @@ -0,0 +1,86 @@ +# Lint as: python3 +"""A sensor that measures the acceleration of the robot base.""" + +from typing import Any, Callable, Sequence, Type, Text, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.utilities import noise_generators + +_ACCELEROMETER_DIM = 3 +_DEFAULT_ACCELEROMETER_LOWER_BOUND = (-1, -1, -1) +_DEFAULT_ACCELEROMETER_UPPER_BOUND = (1, 1, 1) + + +@gin.configurable +class AccelerometerSensor(sensor.Sensor): + """An Accelerometer sensor.""" + + def __init__( + self, + name: Text = "Accelerometer", + dtype: Type[Any] = np.float64, + lower_bound: Sequence[float] = _DEFAULT_ACCELEROMETER_LOWER_BOUND, + upper_bound: Sequence[float] = _DEFAULT_ACCELEROMETER_UPPER_BOUND, + noise_generator: Union[Callable[..., Any], + noise_generators.NoiseGenerator] = None, + sensor_latency: Union[float, Sequence[float]] = 0.0, + ): + """Constructs AccelerometerSensor. + + Generates separate IMU value channels as per configuration. + + Args: + name: the name of the sensor. + dtype: data type of sensor value. + lower_bound: The lower bounds of the sensor reading. + upper_bound: The upper bounds of the sensor reading. + noise_generator: Used to add noise to the readings. + sensor_latency: There are two ways to use this expected sensor latency. + For both methods, the latency should be in the same unit as the sensor + data timestamp. 1. As a single float number, the observation will be a + 1D array. For real robots, this should be set to 0.0. 2. As a array of + floats, the observation will be a 2D array based on how long the history + need to be. Thus, [0.0, 0.1, 0.2] is a history length of 3. + + """ + super().__init__( + name=name, + sensor_latency=sensor_latency, + interpolator_fn=sensor.linear_obs_blender) + + self._noise_generator = noise_generator + self._dtype = dtype + + if lower_bound is None or upper_bound is None: + raise ValueError("Must provides bounds for the Accelerometer readings.") + + if len(lower_bound) != _ACCELEROMETER_DIM or len( + upper_bound) != _ACCELEROMETER_DIM: + raise ValueError( + "Bounds must be {} dimensions.".format(_ACCELEROMETER_DIM)) + + lower_bound = np.array(lower_bound, dtype=dtype) + upper_bound = np.array(upper_bound, dtype=dtype) + + self._observation_space = self._stack_space( + gym.spaces.Box( + low=np.array(lower_bound, dtype=self._dtype), + high=np.array(upper_bound, dtype=self._dtype), + dtype=self._dtype)) + + def _get_original_observation(self): + return self._robot.timestamp, np.array( + self._robot.base_acceleration_accelerometer, dtype=self._dtype) + + def get_observation(self) -> np.ndarray: + delayed_observation = super().get_observation() + if self._noise_generator: + if callable(self._noise_generator): + return self._noise_generator(delayed_observation) + else: + return self._noise_generator.add_noise(delayed_observation) + return delayed_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/camera_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/camera_sensor.py new file mode 100644 index 000000000..d1c6bde8c --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/camera_sensor.py @@ -0,0 +1,184 @@ +# Lint as: python3 +"""A sensor for robot-mounted 1D lidar (laser scan).""" + +from typing import Any, Iterable, Sequence, Type, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.vision import point_cloud_utils +from pybullet_envs.minitaur.vision import sim_camera + +_MODE_TO_NUM_CHANNELS_DICT = { + sim_camera.CameraMode.DEPTH: 1, + sim_camera.CameraMode.RGB: 3, + sim_camera.CameraMode.RGBD: 4, + sim_camera.CameraMode.POINTCLOUD_WORLD_FRAME: 3, + sim_camera.CameraMode.POINTCLOUD_ROBOT_FRAME: 3, +} + + +@gin.configurable +class CameraSensor(sensor.Sensor): + """A robot-mounted sensor that returns RGBD images. + + Attributes: + resolution: A 2-tuple (width, height) that represents the resolution of the + camera. + fov_degree: A floating point value that represents the field of view of the + camera in the vertical direction. The unit is degree. + """ + + def __init__(self, + camera_translation_from_base, + camera_rotation_from_base, + parent_link_id=-1, + camera_mode=sim_camera.CameraMode.DEPTH, + camera_update_frequency_hz=10, + camera_stabilized=False, + fov_degree=60, + resolution=(32, 32), + lower_bound: Union[float, Iterable[float]] = 0.0, + upper_bound: Union[float, Iterable[float]] = 255.0, + sensor_latency: Union[float, Sequence[float]] = 0.0, + dtype: Type[Any] = np.float64, + name="vision"): + """Initializes the CameraSensor. + + Args: + camera_translation_from_base: A 3-vector translation from the center of + the specified link. + camera_rotation_from_base: A 4-vector quaternion represents the rotation + of the camera relative to the specified link. + parent_link_id: The pybullet link id, where the camera is mounted on. + camera_mode: An enum that specifies the mode that the camera operates. See + sim_camera.CameraMode for more details. + camera_update_frequency_hz: The frequency at which the camera will capture + a frame. + camera_stabilized: Whether the camera is stabilized. See + sim_camera.MountedCamera for more details. + fov_degree: The vertical field of view of the camera (in degree). + resolution: A 2-tuple that represents the width and the height of the + camera image. + lower_bound: The lower bound of values of the camera output. It could be a + single float or an array of floats with shape (height, width, channels). + upper_bound: The upper bound of values of the camera output. It could be a + single float or an array of floats with shape (height, width, + channels).. + sensor_latency: See base class. + dtype: See base class. + name: The name of the sensor. + """ + super().__init__( + name=name, + sensor_latency=sensor_latency, + interpolator_fn=sensor.closest_obs_blender) + self._parent_link_id = parent_link_id + self._camera_mode = camera_mode + self._camera_translation_from_base = camera_translation_from_base + self._camera_rotation_from_base = camera_rotation_from_base + self.camera_update_frequency_hz = camera_update_frequency_hz + self._time_interval_every_camera_update = (1.0 / + self.camera_update_frequency_hz) + self._camera_stabilized = camera_stabilized + self._fov_degree = fov_degree + self._resolution = resolution + num_channels = _MODE_TO_NUM_CHANNELS_DICT[self._camera_mode] + self._camera = None + self._camera_image = None + self._dtype = dtype + if isinstance(lower_bound, list): + lower_bound = np.array(lower_bound, dtype=self._dtype) + else: + lower_bound = lower_bound * np.ones( + shape=(resolution[1], resolution[0], num_channels), dtype=self._dtype) + if isinstance(upper_bound, list): + upper_bound = np.array(upper_bound, dtype=self._dtype) + else: + upper_bound = upper_bound * np.ones( + shape=(resolution[1], resolution[0], num_channels), dtype=self._dtype) + self._observation_space = gym.spaces.Box( + low=lower_bound, high=upper_bound, dtype=self._dtype) + self._last_camera_image_timestamp = None + + def change_mounting_point( + self, + camera_translation_from_link: Sequence[float] = (0, 0, 0), + camera_rotation_from_link: Sequence[float] = (0, 0, 0, 1), + parent_link_id: int = -1): + """Changes mounting point. Must be called before calls to set_robot(). + + Args: + camera_translation_from_link: A 3-vector translation from the center of + the specified link. + camera_rotation_from_link: A 4-vector quaternion represents the rotation + of the camera relative to the specified link. + parent_link_id: The pybullet link id, where the camera is mounted on. + """ + self._parent_link_id = parent_link_id + self._camera_translation_from_base = camera_translation_from_link + self._camera_rotation_from_base = camera_rotation_from_link + + def set_robot(self, robot): + super().set_robot(robot) + + self._camera = sim_camera.MountedCamera( + pybullet_client=robot.pybullet_client, + body_id=robot.robot_id, + parent_link_id=self._parent_link_id, + relative_translation=self._camera_translation_from_base, + relative_rotation=self._camera_rotation_from_base, + stabilized=self._camera_stabilized, + camera_mode=self._camera_mode, + fov_degree=self._fov_degree, + resolution=self._resolution) + + def on_reset(self, env): + self._env = env + self._last_camera_image_timestamp = None + super().on_reset(env) + + def _get_original_observation(self): + if self._last_camera_image_timestamp is None or ( + self._robot.timestamp >= self._last_camera_image_timestamp + + self._time_interval_every_camera_update): + self._camera_image = self._camera.render_image().astype(self._dtype) + self._last_camera_image_timestamp = self._robot.timestamp + return self._robot.timestamp, self._camera_image + + def project_depth_map_to_point_cloud(self, depth_map, use_world_frame=True): + """Convert the depth map into a 3D point cloud. + + Args: + depth_map: A 2D numpy array with shape (height, width) which represents + the depth map. + use_world_frame: Whether converts the depth map into a point cloud in the + world frame. If False, the point cloud is in the robot's local frame. If + True, the point cloud is in the world frame if the robot's base + position/orientation can be measured (e.g. in sim, using SLAM or mocap). + + Returns: + A point cloud represented by a numpy array of shape (height, width, 3). + """ + point_cloud = point_cloud_utils.distance_map_to_point_cloud( + np.squeeze(depth_map), self.fov_degree / 180.0 * np.pi, + depth_map.shape[1], depth_map.shape[0]) + if use_world_frame: + point_cloud = ( + self._camera.transform_point_cloud_from_camera_to_world_frame( + point_cloud)) + else: + point_cloud = ( + self._camera.transform_point_cloud_from_camera_to_robot_frame( + point_cloud)) + return point_cloud + + @property + def resolution(self): + return self._camera.resolution + + @property + def fov_degree(self): + return self._camera.fov_degree diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/imu_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/imu_sensor.py new file mode 100644 index 000000000..a26cf491d --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/imu_sensor.py @@ -0,0 +1,133 @@ +# Lint as: python3 +"""The on robot sensor classes.""" + +import enum +from typing import Any, Callable, Iterable, Sequence, Type, Text, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.utilities import noise_generators + + +@gin.constants_from_enum +class IMUChannel(enum.Enum): + ROLL = 1, + PITCH = 2, + YAW = 3, + ROLL_RATE = 4, + PITCH_RATE = 5, + YAW_RATE = 6, + + +@gin.configurable +class IMUSensor(sensor.Sensor): + """An IMU sensor.""" + + def __init__( + self, + name: Text = "IMU", + dtype: Type[Any] = np.float64, + channels: Sequence[IMUChannel] = None, + lower_bound: Union[float, Iterable[float]] = None, + upper_bound: Union[float, Iterable[float]] = None, + noise_generator: Union[Callable[..., Any], + noise_generators.NoiseGenerator] = None, + sensor_latency: Union[float, Sequence[float]] = 0.0, + ): + """Constructs IMUSensor. + + Generates separate IMU value channels as per configuration. + + Args: + name: the name of the sensor. + dtype: data type of sensor value. + channels: value channels wants to subscribe. Must be members of the + IMUChannel class. + lower_bound: The lower bounds of the sensor reading. + upper_bound: The upper bounds of the sensor reading. + noise_generator: Used to add noise to the readings. + sensor_latency: There are two ways to use this expected sensor latency. + For both methods, the latency should be in the same unit as the sensor + data timestamp. 1. As a single float number, the observation will be a + 1D array. For real robots, this should be set to 0.0. 2. As a array of + floats, the observation will be a 2D array based on how long the history + need to be. Thus, [0.0, 0.1, 0.2] is a history length of 3. + + Raises: + ValueError: If no IMU channel is provided and no bounds for the channels. + """ + super().__init__( + name=name, + sensor_latency=sensor_latency, + interpolator_fn=sensor.linear_obs_blender) + if channels is None: + raise ValueError("IMU channels are not provided.") + self._channels = channels + self._num_channels = len(self._channels) + self._noise_generator = noise_generator + self._dtype = dtype + + if lower_bound is None or upper_bound is None: + raise ValueError("Must provides bounds for the IMU readings.") + + if isinstance(lower_bound, (float, int)): + lower_bound = np.full(self._num_channels, lower_bound, dtype=dtype) + else: + lower_bound = np.array(lower_bound, dtype=dtype) + + if len(lower_bound) != self._num_channels: + raise ValueError("length of sensor lower bound {lower_bound} does not" + " match the number of channels.") + + if isinstance(upper_bound, (float, int)): + upper_bound = np.full(self._num_channels, upper_bound, dtype=dtype) + else: + upper_bound = np.array(upper_bound, dtype=dtype) + + if len(upper_bound) != self._num_channels: + raise ValueError("length of sensor upper bound {upper_bound} does not" + " match the number of channels.") + + self._observation_space = self._stack_space( + gym.spaces.Box( + low=np.array(lower_bound, dtype=self._dtype), + high=np.array(upper_bound, dtype=self._dtype), + dtype=self._dtype)) + + def get_channels(self) -> Sequence[IMUChannel]: + return self._channels + + def get_num_channels(self) -> int: + return self._num_channels + + def _get_original_observation(self): + rpy = self._robot.base_roll_pitch_yaw + observations = np.zeros(self._num_channels) + for i, channel in enumerate(self._channels): + if channel == IMUChannel.ROLL: + observations[i] = rpy[0] + elif channel == IMUChannel.PITCH: + observations[i] = rpy[1] + elif channel == IMUChannel.YAW: + observations[i] = rpy[2] + elif channel == IMUChannel.ROLL_RATE: + observations[i] = self._robot.base_roll_pitch_yaw_rate[0] + elif channel == IMUChannel.PITCH_RATE: + observations[i] = self._robot.base_roll_pitch_yaw_rate[1] + elif channel == IMUChannel.YAW_RATE: + observations[i] = self._robot.base_roll_pitch_yaw_rate[2] + + return self._robot.timestamp, np.array(observations, dtype=self._dtype) + + def get_observation(self) -> np.ndarray: + delayed_observation = super().get_observation() + if self._noise_generator: + if callable(self._noise_generator): + return self._noise_generator(delayed_observation) + else: + return self._noise_generator.add_noise(delayed_observation) + + return delayed_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/last_action_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/last_action_sensor.py new file mode 100644 index 000000000..e61299f02 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/last_action_sensor.py @@ -0,0 +1,60 @@ +# Lint as: python3 +"""A sensor that returns the last action(s) sent to the environment.""" + +from typing import Any, Dict, Sequence, Text, Type, Tuple, Union +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.sensors import space_utils + + +@gin.configurable +class LastActionSensor(sensor.Sensor): + """A sensor that reports the last action taken.""" + + def __init__(self, + name: Text = "LastAction", + dtype: Type[Any] = np.float64, + sensor_latency: Union[float, Sequence[float]] = 0): + """Constructs LastActionSensor. + + We do not provide a robot instance during __init__, as robot instances may + be reloaded/recreated during the simulation. + + Args: + name: the name of the sensor + dtype: data type of sensor value. + sensor_latency: There are two ways to use this expected sensor latency. + For both methods, the latency should be in the same unit as the sensor + data timestamp. 1. As a single float number, the observation will be a + 1D array. For real robots, this should be set to 0.0. 2. As a array of + floats, the observation will be a 2D array based on how long the history + need to be. Thus, [0.0, 0.1, 0.2] is a history length of 3. + """ + super().__init__(name=name, + sensor_latency=sensor_latency, + # We generally don't interpolate actions. + interpolator_fn=sensor.older_obs_blender) + + self._dtype = dtype + self._env = None + + def on_reset(self, env: gym.Env): + """From the callback, the sensor remembers the environment. + + Args: + env: the environment who invokes this callback function. + """ + # Constructs the observation space using the env's action space. + self._observation_space = self._stack_space( + env.action_space, dtype=self._dtype) + + # Call the super class methods to initialize the buffers + super().on_reset(env) + + def _get_original_observation( + self) -> Tuple[float, Union[Dict[Text, np.ndarray], np.ndarray]]: + return self._env.get_time(), space_utils.action_astype( + self._env.last_action, self._dtype) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/motor_angle_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/motor_angle_sensor.py new file mode 100644 index 000000000..ce1754547 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/motor_angle_sensor.py @@ -0,0 +1,122 @@ +# Lint as: python3 +"""The on robot sensor classes.""" + +from typing import Any, Callable, Iterable, Optional, Sequence, Type, Text, Tuple, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor + +from pybullet_envs.minitaur.envs_v2.utilities import noise_generators + + +def _convert_to_np_array(inputs: Union[float, Tuple[float], np.ndarray], dim): + """Converts the inputs to a numpy array. + + Args: + inputs: The input scalar or array. + dim: The dimension of the converted numpy array. + + Returns: + The converted numpy array. + + Raises: + ValueError: If the inputs is an array whose dimension does not match the + provided dimension. + """ + outputs = None + if isinstance(inputs, (tuple, np.ndarray)): + outputs = np.array(inputs) + else: + outputs = np.full(dim, inputs) + + if len(outputs) != dim: + raise ValueError("The inputs array has a different dimension {}" + " than provided, which is {}.".format(len(outputs), dim)) + + return outputs + + +@gin.configurable +class MotorAngleSensor(sensor.Sensor): + """A sensor that reads motor angles from the robot.""" + + def __init__(self, + name: Text = "MotorAngle", + dtype: Type[Any] = np.float64, + lower_bound: Optional[Union[float, Iterable[float]]] = None, + upper_bound: Optional[Union[float, Iterable[float]]] = None, + noise_generator: Union[Callable[..., Any], + noise_generators.NoiseGenerator] = None, + sensor_latency: Union[float, Sequence[float]] = 0.0, + observe_sine_cosine: bool = False): + """Initializes the class. + + Args: + name: The name of the sensor. + dtype: The datatype of this sensor. + lower_bound: The optional lower bounds of the sensor reading. If not + provided, will extract from the motor limits of the robot class. + upper_bound: The optional upper bounds of the sensor reading. If not + provided, will extract from the motor limits of the robot class. + noise_generator: Adds noise to the sensor readings. + sensor_latency: There are two ways to use this expected sensor latency. + For both methods, the latency should be in the same unit as the sensor + data timestamp. 1. As a single float number, the observation will be a + 1D array. For real robots, this should be set to 0.0. 2. As a array of + floats, the observation will be a 2D array based on how long the history + need to be. Thus, [0.0, 0.1, 0.2] is a history length of 3. + observe_sine_cosine: whether to observe motor angles as sine and cosine + values. + """ + super().__init__( + name=name, + sensor_latency=sensor_latency, + interpolator_fn=sensor.linear_obs_blender) + self._noise_generator = noise_generator + self._dtype = dtype + self._lower_bound = lower_bound + self._upper_bound = upper_bound + self._observe_sine_cosine = observe_sine_cosine + + def set_robot(self, robot): + self._robot = robot + # Creates the observation space based on the robot motor limitations. + if self._observe_sine_cosine: + lower_bound = _convert_to_np_array(-1, 2 * self._robot.num_motors) + elif self._lower_bound: + lower_bound = _convert_to_np_array(self._lower_bound, + self._robot.num_motors) + else: + lower_bound = _convert_to_np_array( + self._robot.motor_limits.angle_lower_limits, self._robot.num_motors) + if self._observe_sine_cosine: + upper_bound = _convert_to_np_array(1, 2 * self._robot.num_motors) + elif self._upper_bound: + upper_bound = _convert_to_np_array(self._upper_bound, + self._robot.num_motors) + else: + upper_bound = _convert_to_np_array( + self._robot.motor_limits.angle_upper_limits, self._robot.num_motors) + + self._observation_space = self._stack_space( + gym.spaces.Box(low=lower_bound, high=upper_bound, dtype=self._dtype)) + + def _get_original_observation(self): + if self._observe_sine_cosine: + return self._robot.timestamp, np.hstack( + (np.cos(self._robot.motor_angles), np.sin(self._robot.motor_angles))) + else: + return self._robot.timestamp, self._robot.motor_angles + + def get_observation(self) -> np.ndarray: + delayed_observation = super().get_observation() + if self._noise_generator: + if callable(self._noise_generator): + return self._noise_generator(delayed_observation) + else: + return self._noise_generator.add_noise(delayed_observation) + + return delayed_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/sensor.py new file mode 100644 index 000000000..e3208cdc3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/sensor.py @@ -0,0 +1,451 @@ +# Lint as: python3 +"""A sensor prototype class. + +The concept is explained in: go/minitaur-gym-redesign-1.1 +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import Any, Iterable, Optional, Sequence, Text, Tuple, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.robots import robot_base +from pybullet_envs.minitaur.robots import time_ordered_buffer + +_ARRAY = Sequence[float] +_FloatOrArray = Union[float, _ARRAY] +_DataTypeList = Iterable[Any] + +# For sensor with multiput outputs, key of the main observation in output dict. +MAIN_OBS_KEY = "" + +# This allows referencing np.float32 in gin config files. For example: +# lidar_sensor.LidarSensor.dtype = @np.float32 +gin.external_configurable(np.float32, module="np") +gin.external_configurable(np.float64, module="np") +gin.external_configurable(np.uint8, module="np") + + +# Observation blenders take a pair of low/high values. The low/high is measured +# by the latency of the observation. So the low value is actually newer in time +# and high value older. The coeff [0, 1] can be thinked as the distance between +# the low and high value value, with 0 being 100% low value and 1 as 100% high +# value. +def linear_obs_blender(low_value: Any, high_value: Any, coeff: float): + """Linear interpolation of low/high values based on coefficient value.""" + return low_value * (1 - coeff) + high_value * coeff + + +def closest_obs_blender(low_value: Any, high_value: Any, coeff: float): + """Choosing the high or low value based on coefficient value.""" + return low_value if coeff < 0.5 else high_value + + +def newer_obs_blender(low_value: Any, unused_high_value: Any, + unused_coeff: float): + """Always choosing low value, which is the newer value between low/high.""" + return low_value + + +def older_obs_blender(unused_low_value: Any, high_value: Any, + unused_coeff: float): + """Always choosing the high value, which is the older value between low/high.""" + return high_value + + +@gin.configurable +class Sensor(object): + """A prototype class of sensors.""" + + def __init__( + self, + name: Text, + sensor_latency: _FloatOrArray, + interpolator_fn: Any, + enable_debug_visualization: bool = False, + ): + """A basic constructor of the sensor. + + We do not provide a robot instance during __init__, as robot instances may + be reloaded/recreated during the simulation. + + Args: + name: the name of the sensor + sensor_latency: There are two ways to use this expected sensor latency. + For both methods, the latency should be in the same unit as the sensor + data timestamp. 1. As a single float number, the observation will be a + 1D array. For real robots, this should be set to 0.0. 2. As an array of + floats, the observation will be a 2D array based on how long the history + need to be. Thus, [0.0, 0.1, 0.2] is a history length of 3. Observations + are stacked on a new axis appended after existing axes. + interpolator_fn: Function that controls how to interpolate the two values + that is returned from the time ordered buffer. + enable_debug_visualization: Whether to draw debugging visualization. + """ + self._robot = None + self._name = name + # Observation space will be implemented by derived classes. + self._observation_space = None + self._sensor_latency = sensor_latency + self._single_latency = True if isinstance(sensor_latency, + (float, int)) else False + self._enable_debug_visualization = enable_debug_visualization + if not self._is_valid_latency(): + raise ValueError("sensor_latency is expected to be a non-negative number " + "or a non-empty list of non-negative numbers.") + self._interpolator_fn = interpolator_fn or newer_obs_blender + self._axis = -1 + timespan = sensor_latency if self._single_latency else max(sensor_latency) + self._observation_buffer = time_ordered_buffer.TimeOrderedBuffer( + max_buffer_timespan=timespan) + + def _is_valid_latency(self): + if self._single_latency: + return self._sensor_latency >= 0 + if self._sensor_latency: + return all(value >= 0 for value in self._sensor_latency) + return False + + def get_name(self) -> Text: + return self._name + + @property + def is_single_latency(self) -> bool: + return self._single_latency + + @property + def observation_space(self) -> gym.spaces.Space: + return self._observation_space + + @property + def enable_debug_visualization(self): + return self._enable_debug_visualization + + @enable_debug_visualization.setter + def enable_debug_visualization(self, enable): + self._enable_debug_visualization = enable + + def get_observation_datatype(self): + """Returns the data type for the numpy structured array. + + It is recommended to define a list of tuples: (name, datatype, shape) + Reference: https://docs.scipy.org/doc/numpy-1.15.0/user/basics.rec.html + Ex: + return [('motor_angles', np.float64, (8, ))] # motor angle sensor + return [('IMU_x', np.float64), ('IMU_z', np.float64), ] # IMU + Will be deprecated (b/150818246) in favor of observation_space. + + Returns: + datatype: a list of data types. + """ + raise NotImplementedError("Deprecated. Are you using the old robot class?") + + def get_lower_bound(self): + """Returns the lower bound of the observation. + + Will be deprecated (b/150818246) in favor of observation_space. + + Returns: + lower_bound: the lower bound of sensor values in np.array format + """ + raise NotImplementedError("Deprecated. Are you using the old robot class?") + + def get_upper_bound(self): + """Returns the upper bound of the observation. + + Will be deprecated (b/150818246) in favor of observation_space. + + Returns: + upper_bound: the upper bound of sensor values in np.array format + """ + raise NotImplementedError("Deprecated. Are you using the old robot class?") + + def _get_original_observation(self) -> Tuple[float, Any]: + """Gets the non-modified observation. + + Different from the get_observation, which can pollute and sensor data with + noise and latency, this method shall return the best effort measurements of + the sensor. For simulated robots, this will return the clean data. For reals + robots, just return the measurements as is. All inherited class shall + implement this method. + + Returns: + The timestamp and the original sensor measurements. + + Raises: + NotImplementedError for the base class. + + """ + raise NotImplementedError("Not implemented for base class." "") + + def get_observation(self): + """Returns the observation data. + + Returns: + observation: the observed sensor values in np.array format + """ + obs = self._observation_buffer.get_delayed_value(self._sensor_latency) + + if self._single_latency: + if isinstance(self._observation_space, gym.spaces.Dict): + return self._interpolator_fn(obs.value_0, obs.value_1, obs.coeff) + else: + return np.asarray( + self._interpolator_fn(obs.value_0, obs.value_1, obs.coeff)) + else: + if isinstance(self._observation_space, gym.spaces.Dict): + # interpolate individual sub observation + interpolated = [ + self._interpolator_fn(data.value_0, data.value_1, data.coeff) + for data in obs + ] + + stacked_per_sub_obs = {} + for k in interpolated[0]: + stacked_per_sub_obs[k] = np.stack( + np.asarray([d[k] for d in interpolated]), axis=self._axis) + return stacked_per_sub_obs + else: + obs = np.asarray([ + self._interpolator_fn(data.value_0, data.value_1, data.coeff) + for data in obs + ]) + return np.stack(obs, axis=self._axis) + + def set_robot(self, robot: robot_base.RobotBase): + """Set a robot instance.""" + self._robot = robot + + def get_robot(self): + """Returns the robot instance.""" + return self._robot + + def on_reset(self, env): + """A callback function for the reset event. + + Args: + env: the environment who invokes this callback function. + """ + self._env = env + self._observation_buffer.reset() + self.on_new_observation() + + def on_step(self, env): + """A callback function for the control step event. + + Args: + env: the environment who invokes this callback function. + """ + pass + + def visualize(self): + """Visualizes the sensor information.""" + pass + + def on_new_observation(self): + """A callback for each observation received. + + To be differentiated from on_step, which will be called only once per + control step (i.e. env.step), this API will be called everytime in the + substep/action repeat loop, when new observations are expected. Each derived + sensor class should implement this API by implementing: + + my_obs = call env/robot api to get the observation + self._observation_buffer.add(my_obs) + """ + timestamp, obs = self._get_original_observation() + if self._enable_debug_visualization: + self.visualize() + self._observation_buffer.add(timestamp, obs) + + def on_terminate(self, env): + """A callback function for the terminate event. + + Args: + env: the environment who invokes this callback function. + """ + pass + + def _stack_space(self, + space: Union[gym.spaces.Box, gym.spaces.Dict], + dtype: np.dtype = None) -> Any: + """Returns stacked version of observation space. + + This stacks a gym.spaces.Box or gym.spaces.Dict action space based on the + length of the sensor latency and the axis for stacking specified in the + sensor. A gym.spaces.Box is just stacked, but a gym.spaces.Dict is + recursively stacked, preserving its dictionary structure while stacking + any gym.spaces.Box contained within. For example, the input action space: + + gym.spaces.Dict({ + 'space_1': gym.spaces.Box(low=0, high=10, shape=(1,)), + 'space_2': gym.spaces.Dict({ + 'space_3': gym.spaces.Box(low=0, high=10, shape=(2,)), + }), + })) + + would be converted to the following if sensor latency was [0, 1]: + + gym.spaces.Dict({ + 'space_1': gym.spaces.Box(low=0, high=10, shape=(1, 2)), + 'space_2': gym.spaces.Dict({ + 'space_3': gym.spaces.Box(low=0, high=10, shape=(2, 2)), + }), + })) + + Args: + space: A gym.spaces.Dict or gym.spaces.Box to be stacked. + dtype: Datatype for the stacking. + + Returns: + stacked_space: A stacked version of the action space. + """ + if self._single_latency: + return space + + # Allow sensors such as last_action_sensor to override the dtype. + dtype = dtype or space.dtype + + if isinstance(space, gym.spaces.Box): + return self._stack_space_box(space, dtype) + elif isinstance(space, gym.spaces.Dict): + return self._stack_space_dict(space, dtype) + else: + raise ValueError(f"Space {space} is an unsupported type.") + + def _stack_space_box(self, space: gym.spaces.Box, + dtype: np.dtype) -> gym.spaces.Box: + """Returns stacked version of a box observation space. + + This stacks a gym.spaces.Box action space based on the length of the sensor + latency and the axis for stacking specified in the sensor. + + Args: + space: A gym.spaces.Box to be stacked. + dtype: Datatype for the stacking + + Returns: + stacked_space: A stacked version of the gym.spaces.Box action space. + """ + length = len(self._sensor_latency) + stacked_space = gym.spaces.Box( + low=np.repeat( + np.expand_dims(space.low, axis=self._axis), length, + axis=self._axis), + high=np.repeat( + np.expand_dims(space.high, axis=self._axis), + length, + axis=self._axis), + dtype=dtype) + + return stacked_space + + def _stack_space_dict(self, space: gym.spaces.Dict, + dtype: np.dtype) -> gym.spaces.Dict: + """Returns stacked version of a dict observation space. + + This stacks a gym.spaces.Dict action space based on the length of the sensor + latency and the recursive structure of the gym.spaces.Dict itself. + + Args: + space: A gym.spaces.Dict to be stacked. + dtype: Datatype for the stacking. + + Returns: + stacked_space: A stacked version of the dictionary action space. + """ + return gym.spaces.Dict([ + (k, self._stack_space(v, dtype)) for k, v in space.spaces.items() + ]) + + def _encode_obs_dict_keys(self, obs_dict): + """Encodes sub obs keys of observation dict or observsation space dict.""" + return {encode_sub_obs_key(self, k): v for k, v in obs_dict.items()} + + +class BoxSpaceSensor(Sensor): + """A prototype class of sensors with Box shapes.""" + + def __init__(self, + name: Text, + shape: Tuple[int, ...], + lower_bound: _FloatOrArray = -np.pi, + upper_bound: _FloatOrArray = np.pi, + dtype=np.float64) -> None: + """Constructs a box type sensor. + + Will be deprecated (b/150818246) once we switch to gym spaces. + Args: + name: the name of the sensor + shape: the shape of the sensor values + lower_bound: the lower_bound of sensor value, in float or np.array. + upper_bound: the upper_bound of sensor value, in float or np.array. + dtype: data type of sensor value + """ + super(BoxSpaceSensor, self).__init__( + name=name, sensor_latency=0.0, interpolator_fn=newer_obs_blender) + self._shape = shape + self._dtype = dtype + + if isinstance(lower_bound, float): + self._lower_bound = np.full(shape, lower_bound, dtype=dtype) + else: + self._lower_bound = np.array(lower_bound) + + if isinstance(upper_bound, float): + self._upper_bound = np.full(shape, upper_bound, dtype=dtype) + else: + self._upper_bound = np.array(upper_bound) + + def set_robot(self, robot): + # Since all old robot class do not inherit from RobotBase, we can enforce + # the checking here. + if isinstance(robot, robot_base.RobotBase): + raise ValueError( + "Cannot use new robot interface RobotBase with old sensor calss.") + self._robot = robot + + def get_shape(self) -> Tuple[int, ...]: + return self._shape + + def get_dimension(self) -> int: + return len(self._shape) + + def get_dtype(self): + return self._dtype + + def get_observation_datatype(self) -> _DataTypeList: + """Returns box-shape data type.""" + return [(self._name, self._dtype, self._shape)] + + def get_lower_bound(self) -> _ARRAY: + """Returns the computed lower bound.""" + return self._lower_bound + + def get_upper_bound(self) -> _ARRAY: + """Returns the computed upper bound.""" + return self._upper_bound + + def get_observation(self) -> np.ndarray: + return np.asarray(self._get_observation(), dtype=self._dtype) + + def _get_original_observation(self) -> Tuple[float, Any]: + # Maintains compatibility with the new sensor classes.""" + raise NotImplementedError("Not implemented for this class.") + + def on_new_observation(self): + # Maintains compatibility with the new sensor classes.""" + pass + + +def encode_sub_obs_key(s: Sensor, sub_obs_name: Optional[Text]): + """Returns a sub observation key for use in observation dictionary.""" + if sub_obs_name == MAIN_OBS_KEY: + return s.get_name() + else: + return f"{s.get_name()}/{sub_obs_name}" diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/space_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/space_utils.py new file mode 100644 index 000000000..270bee2f1 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/space_utils.py @@ -0,0 +1,137 @@ +# Lint as: python3 +"""Converts a list of sensors to gym space.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from typing import List +import gin +import gym +from gym import spaces +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor + + +class UnsupportedConversionError(NotImplementedError): + """An exception when the function cannot convert sensors to the gym space.""" + + +class AmbiguousDataTypeError(TypeError): + """An exception when the function cannot determine the data type.""" + + +@gin.configurable +def convert_sensors_to_gym_space(sensors: List[sensor.Sensor]) -> gym.Space: + """Convert a list of sensors to the corresponding gym space. + + Args: + sensors: a list of the current sensors + + Returns: + space: the converted gym space + + Raises: + UnsupportedConversionError: raises when the function cannot convert the + given list of sensors. + """ + + if all([ + isinstance(s, sensor.BoxSpaceSensor) and s.get_dimension() == 1 + for s in sensors + ]): + return convert_1d_box_sensors_to_gym_space(sensors) + raise UnsupportedConversionError('sensors = ' + str(sensors)) + + +@gin.configurable +def convert_1d_box_sensors_to_gym_space( + sensors: List[sensor.Sensor]) -> gym.Space: + """Convert a list of 1D BoxSpaceSensors to the corresponding gym space. + + Args: + sensors: a list of the current sensors + + Returns: + space: the converted gym space + + Raises: + UnsupportedConversionError: raises when the function cannot convert the + given list of sensors. + AmbiguousDataTypeError: raises when the function cannot determine the + data types because they are not uniform. + """ + # Check if all sensors are 1D BoxSpaceSensors + if not all([ + isinstance(s, sensor.BoxSpaceSensor) and s.get_dimension() == 1 + for s in sensors + ]): + raise UnsupportedConversionError('sensors = ' + str(sensors)) + + # Check if all sensors have the same data type + sensor_dtypes = [s.get_dtype() for s in sensors] + if sensor_dtypes.count(sensor_dtypes[0]) != len(sensor_dtypes): + raise AmbiguousDataTypeError('sensor datatypes are inhomogeneous') + + lower_bound = np.concatenate([s.get_lower_bound() for s in sensors]) + upper_bound = np.concatenate([s.get_upper_bound() for s in sensors]) + observation_space = spaces.Box( + np.array(lower_bound), np.array(upper_bound), dtype=np.float32) + return observation_space + + +@gin.configurable +def convert_sensors_to_gym_space_dictionary( + sensors: List[sensor.Sensor]) -> gym.Space: + """Convert a list of sensors to the corresponding gym space dictionary. + + Args: + sensors: a list of the current sensors + + Returns: + space: the converted gym space dictionary + + Raises: + UnsupportedConversionError: raises when the function cannot convert the + given list of sensors. + """ + gym_space_dict = {} + for s in sensors: + if isinstance(s, sensor.BoxSpaceSensor): + gym_space_dict[s.get_name()] = spaces.Box( + np.array(s.get_lower_bound()), + np.array(s.get_upper_bound()), + dtype=np.float32) + elif isinstance(s, sensor.Sensor): + if isinstance(s.observation_space, spaces.Box): + gym_space_dict[s.get_name()] = s.observation_space + elif isinstance(s.observation_space, spaces.Dict): + gym_space_dict.update(s.observation_space.spaces) + else: + raise UnsupportedConversionError( + f'Unsupported space type {type(s.observation_space)}, ' + f'must be Box or Dict. sensor = {s}') + else: + raise UnsupportedConversionError('sensors = ' + str(sensors)) + return spaces.Dict(gym_space_dict) + + +def create_constant_action(action_space, action_value=0): + """Create an uniform value action based on the type of action space.""" + if isinstance(action_space, gym.spaces.Dict): + # gym.spaces.Dict has a shape of None, so construct action over subspaces. + return { + sub_name: create_constant_action(sub_space, action_value) + for sub_name, sub_space in action_space.spaces.items() + } + else: # Presumably gym.spaces.Box, but in case it is not ... + return np.full(action_space.shape, action_value) + + +def action_astype(action, dtype): + """Transform an action to a different datatype.""" + if isinstance(action, dict): + return {key: action_astype(value, dtype) for key, value in action.items()} + else: + return np.array(action, dtype=dtype) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/toe_position_sensor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/toe_position_sensor.py new file mode 100644 index 000000000..5e875045a --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/toe_position_sensor.py @@ -0,0 +1,100 @@ +# Lint as: python3 +"""Quadruped toe position sensor.""" + +from typing import Any, Callable, Sequence, Text, Tuple, Type, Union + +import gin +import gym +import numpy as np + +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.envs_v2.utilities import noise_generators + + +def _convert_to_np_array(inputs: Union[float, Tuple[float], np.ndarray], dim): + """Converts the inputs to a numpy array. + + Args: + inputs: The input scalar or array. + dim: The dimension of the converted numpy array. + + Returns: + The converted numpy array. + + Raises: + ValueError: If the inputs is an array whose dimension does not match the + provided dimension. + """ + outputs = None + if isinstance(inputs, (tuple, np.ndarray)): + outputs = np.array(inputs) + else: + outputs = np.full(dim, inputs) + + if len(outputs) != dim: + raise ValueError("The inputs array has a different dimension {}" + " than provided, which is {}.".format(len(outputs), dim)) + + return outputs + + +@gin.configurable +class ToePositionSensor(sensor.Sensor): + """A sensor that outputs the toe positions of attached robots or objects.""" + + def __init__( + self, + name: Text = "toe_position", + dtype: Type[Any] = np.float64, + lower_bound: Union[float, Sequence[float]] = -1.0, + upper_bound: Union[float, Sequence[float]] = 1.0, + noise_generator: Union[Callable[..., Any], + noise_generators.NoiseGenerator] = None, + sensor_latency: Union[float, Sequence[float]] = 0.0, + ): + """Constructor. + + Args: + name: Name of the sensor. + dtype: Data type of sensor value. + lower_bound: The optional lower bounds of the sensor reading. + upper_bound: The optional upper bounds of the sensor reading. + noise_generator: Used to add noise to the readings. + sensor_latency: Single or multiple latency in seconds. See sensor.Sensor + docstring for details. + """ + super().__init__( + name=name, + sensor_latency=sensor_latency, + interpolator_fn=sensor.linear_obs_blender) + self._dtype = dtype + self._lower_bound = lower_bound + self._upper_bound = upper_bound + self._noise_generator = noise_generator + + def set_robot(self, robot): + self._robot = robot + num_legs = len(robot.urdf_loader.get_end_effector_id_dict().values()) + lower_bound = _convert_to_np_array(self._lower_bound, num_legs * 3) + + upper_bound = _convert_to_np_array(self._upper_bound, num_legs * 3) + + self._observation_space = self._stack_space( + gym.spaces.Box(low=lower_bound, high=upper_bound, dtype=self._dtype)) + + def _get_original_observation(self) -> Tuple[float, np.ndarray]: + """Returns raw observation with timestamp.""" + toe_position = np.array( + self._robot.foot_positions(), dtype=self._dtype).flatten() + + return self._robot.timestamp, toe_position + + def get_observation(self) -> np.ndarray: + delayed_observation = super().get_observation() + if self._noise_generator: + if callable(self._noise_generator): + return self._noise_generator(delayed_observation) + else: + return self._noise_generator.add_noise(delayed_observation) + + return delayed_observation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/simple_locomotion_task.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/simple_locomotion_task.py new file mode 100644 index 000000000..9046f9d8d --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/simple_locomotion_task.py @@ -0,0 +1,125 @@ +"""A simple locomotion taskand termination condition.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +import gin +from pybullet_envs.minitaur.envs_v2.tasks import task_interface +from pybullet_envs.minitaur.envs_v2.tasks import task_utils +from pybullet_envs.minitaur.envs_v2.tasks import terminal_conditions +from pybullet_envs.minitaur.envs_v2.utilities import env_utils_v2 as env_utils + + +@gin.configurable +class SimpleForwardTask(task_interface.Task): + """A basic "move forward" task.""" + + def __init__(self, + weight=1.0, + terminal_condition=terminal_conditions + .default_terminal_condition_for_minitaur, + divide_with_dt=False, + clip_velocity=None, + energy_penalty_coef=0.0, + min_com_height=None, + weight_action_accel=None): + """Initializes the task. + + Args: + weight: Float. The scaling factor for the reward. + terminal_condition: Callable object or function. Determines if the task is + done. + divide_with_dt: if True, we divide the velocity reward with dt. + clip_velocity: if not None, we will clip the velocity with this value. + energy_penalty_coef: Coefficient for the energy penalty that will be added + to the reward. 0 by default. + min_com_height: Minimum height for the center of mass of the robot that + will be used to terminate the task. This is used to obtain task specific + gaits and set by the config or gin files based on the task and robot. + weight_action_accel: if not None, penalize the action acceleration. + + Raises: + ValueError: The energey coefficient is smaller than zero. + """ + self._weight = weight + self._terminal_condition = terminal_condition + self._last_base_position = None + self._divide_with_dt = divide_with_dt + self._clip_velocity = clip_velocity + self._weight_action_accel = weight_action_accel + self._action_history_sensor = None + self._min_com_height = min_com_height + self._energy_penalty_coef = energy_penalty_coef + self._env = None + self._step_count = 0 + if energy_penalty_coef < 0: + raise ValueError("Energy Penalty Coefficient should be >= 0") + + def __call__(self, env): + return self.reward(env) + + def reset(self, env): + self._env = env + self._last_base_position = env_utils.get_robot_base_position( + self._env.robot) + + if self._weight_action_accel is not None: + sensor_name = "LastAction" + self._action_history_sensor = env.sensor_by_name(sensor_name) + + @property + def step_count(self): + return self._step_count + + def update(self, env): + """Updates the internal state of the task.""" + del env + self._last_base_position = env_utils.get_robot_base_position( + self._env.robot) + + def reward(self, env): + """Get the reward without side effects.""" + del env + + self._step_count += 1 + env = self._env + current_base_position = env_utils.get_robot_base_position(self._env.robot) + velocity = current_base_position[0] - self._last_base_position[0] + if self._divide_with_dt: + velocity /= env.env_time_step + if self._clip_velocity is not None: + limit = float(self._clip_velocity) + velocity = np.clip(velocity, -limit, limit) + + if self._weight_action_accel is None: + action_acceleration_penalty = 0.0 + else: + past_actions = self._action_history_sensor.get_observation().T + action = past_actions[0] + prev_action = past_actions[1] + prev_prev_action = past_actions[2] + acc = action - 2 * prev_action + prev_prev_action + action_acceleration_penalty = ( + float(self._weight_action_accel) * np.mean(np.abs(acc))) + + reward = velocity + reward -= action_acceleration_penalty + + # Energy + if self._energy_penalty_coef > 0: + energy_reward = -task_utils.calculate_estimated_energy_consumption( + self._env.robot.motor_torques, self._env.robot.motor_velocities, + self._env.sim_time_step, self._env.num_action_repeat) + reward += energy_reward * self._energy_penalty_coef + + return reward * self._weight + + def done(self, env): + del env + position = env_utils.get_robot_base_position(self._env.robot) + if self._min_com_height and position[2] < self._min_com_height: + return True + return self._terminal_condition(self._env) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_interface.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_interface.py new file mode 100644 index 000000000..720d45010 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_interface.py @@ -0,0 +1,37 @@ +# Lint as: python3 +"""Interface that specifies tasks.""" +# TODO(tingnan): Def. proper task interface - see TODO(b/154635313) in comments. + +import abc +from typing import Sequence + +import gym + +from pybullet_envs.minitaur.envs_v2.sensors import sensor + + +class Task(metaclass=abc.ABCMeta): + """Base class for tasks.""" + + # TODO(b/154635313): Deprecate this method. Consolidate it into update(). + @abc.abstractmethod + def reward(self, env: gym.Env) -> float: + """Returns the reward for the current state of the environment.""" + + @abc.abstractmethod + def reset(self, env: gym.Env) -> None: + """Resets the task.""" + + @abc.abstractmethod + def update(self, env: gym.Env) -> None: + """Updates the internal state of the task.""" + + # TODO(b/154635313): Deprecate this method. Consolidate it into update(). + @abc.abstractmethod + def done(self, env: gym.Env) -> bool: + """Determines whether the task is done.""" + + @property + def sensors(self) -> Sequence[sensor.Sensor]: + """Returns sensors used by task.""" + return [] diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_utils.py new file mode 100644 index 000000000..90ac2814a --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/task_utils.py @@ -0,0 +1,108 @@ +"""Common tools and functionalities used in different tasks.""" +import numpy as np + + +def calculate_target_speed_at_timestep(speed_stages): + """Interpolates the speed based on a speed profile and simulation steps. + + Args: + speed_stages: The list of timesteps (in increasing order) and speed (float + or a list of floats for x and y components) at that specific timestep. + Example formats can be found on task_utils_test.py and at the header of + the speed_reward_task.py. + + Returns: + Target speed for the specific step (meters per simulation step). + Raises: + ValueError if the input is not in the expected format. + """ + if len(speed_stages) != 2 or len(speed_stages[0]) != len(speed_stages[1]): + raise ValueError('Speed stages for the task is not in correct format!') + steps = np.array(speed_stages[0]) + speeds = np.array(speed_stages[1]) + num_steps = steps[-1] + if len(speeds.shape) == 1: + return np.interp(range(num_steps), steps, speeds) + speed_at_timestep = np.interp(range(num_steps), steps, speeds[:, 0]).reshape( + (num_steps, 1)) + speed_at_timestep = speed_at_timestep.reshape((num_steps, 1)) + if speeds.shape[1] == 2: + speed_y = np.interp(range(num_steps), steps, speeds[:, 1]).reshape( + (num_steps, 1)) + speed_at_timestep = np.concatenate((speed_at_timestep, speed_y), axis=1) + else: + speed_at_timestep = np.concatenate( + (speed_at_timestep, np.zeros((num_steps, 1))), axis=1) + return speed_at_timestep + + +def calculate_distance(vector_1, vector_2): + """Calculates the distance between 2 vectors. + + This is used to calculate distance between 2 points in 3D space as well as + distances between two orientation vectors. + + Args: + vector_1: First vector that will be used for comparison with the other. + vector_2: Second vector used for comparison. + + Returns: + Distance between the two vectors represented by a float. + """ + return np.linalg.norm(np.array(vector_1) - np.array(vector_2)) + + +def turn_angle(new_vector, reference_vector): + """Calculates the change in orientation of the two vectors. + + This is used to calculate the relative angle perception for the + robot. + + Args: + new_vector: The front vector of the robot at current timestep. + reference_vector: The front vector of the robot at previous timestep. + + Returns: + Angle representing the change in orientation of the robot projected to + x-y plane. + """ + # Project the vectors to x-y plane + v1 = np.resize(new_vector, 3) + v2 = np.resize(reference_vector, 3) + v1[2] = 0 + v2[2] = 0 + # Calculate the right hand rotation between two vectors using z vector + # (0,0,-1) as reference cross product vector. + # Compatible with the yaw rotation of pyBullet. + return np.arctan2(np.dot(np.cross(v2, v1), (0, 0, -1)), np.dot(v1, v2)) + + +def front_vector(pybullet_client, orientation): + """Calculates the front vector of the robot on x-y plane. + + Args: + pybullet_client: Pybullet client instantiation. + orientation: Orientation of the robot in quaternion form. + + Returns: + 3D vector where z component is set to 0. + """ + rot_matrix = pybullet_client.getMatrixFromQuaternion(orientation) + return [rot_matrix[0], -rot_matrix[1], 0] + + +def calculate_estimated_energy_consumption(motor_torques, motor_velocities, + sim_time_step, num_action_repeat): + """Calculates energy consumption based on the args listed. + + Args: + motor_torques: Torques of all the motors + motor_velocities: Velocities of all the motors. + sim_time_step: Simulation time step length (seconds). + num_action_repeat: How many steps the simulation repeats the same action. + + Returns: + Total energy consumption of all the motors (watts). + """ + return np.abs(np.dot(motor_torques, + motor_velocities)) * sim_time_step * num_action_repeat diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/terminal_conditions.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/terminal_conditions.py new file mode 100644 index 000000000..9fe512057 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/tasks/terminal_conditions.py @@ -0,0 +1,301 @@ +"""Contains the terminal conditions for locomotion tasks.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gin +import numpy as np + +from pybullet_envs.minitaur.envs_v2.utilities import minitaur_pose_utils +from pybullet_envs.minitaur.envs_v2.utilities import env_utils_v2 as env_utils +from pybullet_envs.minitaur.envs_v2.utilities import termination_reason as tr + + +@gin.configurable +def default_terminal_condition_for_minitaur(env): + """A default terminal condition for Minitaur. + + Minitaur is considered as fallen if the base position is too low or the base + tilts/rolls too much. + + Args: + env: An instance of MinitaurGymEnv + + Returns: + A boolean indicating if Minitaur is fallen. + """ + orientation = env_utils.get_robot_base_orientation(env.robot) + rot_mat = env.pybullet_client.getMatrixFromQuaternion(orientation) + local_up = rot_mat[6:] + pos = env_utils.get_robot_base_position(env.robot) + return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or + pos[2] < 0.13) + + +@gin.configurable +def terminal_condition_for_minitaur_extended_env(env): + """Returns a bool indicating that the extended env is terminated. + + This predicate checks whether 1) the legs are bent inward too much or + 2) the body is tilted too much. + + Args: + env: An instance of MinitaurGymEnv + """ + motor_angles = env.robot.motor_angles + leg_pose = minitaur_pose_utils.motor_angles_to_leg_pose(motor_angles) + + swing_threshold = np.radians(35.0) + if (leg_pose[0] > swing_threshold or leg_pose[2] > swing_threshold or # Front + leg_pose[1] < -swing_threshold or leg_pose[3] < -swing_threshold): # Rear + return True + roll, _, _ = env.robot.base_roll_pitch_yaw + if abs(roll) > np.radians(30.0): + return True + + return False + + +@gin.configurable +def default_terminal_condition_for_laikago(env): + """A default terminal condition for Laikago. + + Minitaur is considered as fallen if the base position is too low or the base + tilts/rolls too much. + + Args: + env: An instance of MinitaurGymEnv + + Returns: + A boolean indicating if Minitaur is fallen. + """ + roll, pitch, _ = env.robot.base_roll_pitch_yaw + pos = env_utils.get_robot_base_position(env.robot) + return abs(roll) > 0.2 or abs(pitch) > 0.2 or pos[2] < 0.35 + + +@gin.configurable +def default_terminal_condition_for_laikago_v2( + env, + max_roll: float = 1.2, + max_pitch: float = 1.2, + min_height: float = 0.15, + enforce_foot_contacts: bool = False): + """A default terminal condition for Laikago_v2. + + Laikago is considered as fallen if the base position is too low or the base + tilts/rolls too much. + + Args: + env: An instance of MinitaurGymEnv + max_roll: Max roll before the episode terminates. + max_pitch: Max pitch before the episode terminates. + min_height: Min height before the episode terminates. + enforce_foot_contacts: Ensure that contacts are established with the feet. + + Returns: + A boolean indicating if Minitaur is fallen. + """ + # Make sure that contacts are only made with the robot's feet. + unwanted_collision = False + if enforce_foot_contacts: + # Get list of foot and knee link ids. Sometimes, the simulation will + # register a contact as having been made with the knee link, even though it + # was actually the foot that made the contact. Checking both the foot and + # knee links for contact accounts for that. + foot_link_ids = list( + env.robot.urdf_loader.get_end_effector_id_dict().values()) + knee_link_ids = [foot_link_id - 1 for foot_link_id in foot_link_ids] + contacts = env.pybullet_client.getContactPoints(bodyA=env.robot.robot_id) + for contact in contacts: + # Two different bodies made contact (i.e. not a self-collision). + if contact[1] != contact[2]: + foot_contact = (contact[3] in foot_link_ids) or ( + contact[3] in knee_link_ids) + unwanted_collision = unwanted_collision or not foot_contact + + roll, pitch, _ = env.robot.base_roll_pitch_yaw + pos = env.robot.base_position + return (abs(roll) > max_roll or abs(pitch) > max_pitch or + pos[2] < min_height or unwanted_collision) + + +@gin.configurable +def default_terminal_condition_for_agility(env, + max_roll: float = 1.8, + max_pitch: float = 1.8, + min_height: float = 0.0, + enforce_foot_contacts: bool = False): + """A default terminal condition for more agile tasks (i.e. jumping). + + The robot is considered as fallen if the base position is too low, the base + tilts/rolls too much or parts of the body other than the feet touch the + ground. + + Args: + env: An instance of the gym env. + max_roll: Max roll before the episode terminates. + max_pitch: Max pitch before the episode terminates. + min_height: Min height before the episode terminates. + enforce_foot_contacts: Ensure that contacts are established with the feet. + + Returns: + A boolean indicating if the episode should be terminated. + """ + + # Make sure that contacts are only made with the robot's feet. + unwanted_collision = False + if enforce_foot_contacts: + knee_link_ids = [2, 5, 8, 11] + contacts = env.pybullet_client.getContactPoints(bodyA=env.robot.robot_id) + for contact in contacts: + if contact[1] != contact[2]: + foot_contact = contact[3] in knee_link_ids + unwanted_collision = unwanted_collision or not foot_contact + + roll, pitch, _ = env.robot.base_roll_pitch_yaw + pos = env.robot.base_position + return (abs(roll) > max_roll or abs(pitch) > max_pitch or + pos[2] < min_height or unwanted_collision) + + +@gin.configurable +def get_terminal_reason(collisions, task): + termination_reason = None + # Checking collision termination + if collisions: + termination_reason = tr.TerminationReason.AGENT_COLLISION + if task.is_task_success(): + termination_reason = tr.TerminationReason.GOAL_REACHED + return termination_reason + + +@gin.configurable +def maxstep_terminal_condition(env, max_step=500): + """A terminal condition based on the time step. + + Args: + env: An instance of MinitaurGymEnv + max_step: The maximum time step allowed for the environment + + Returns: + A boolean indicating if the env.step exceeds the given limit + """ + return env.env_step_counter > max_step + + +@gin.configurable +class MaxTimeTerminalCondition(object): + """Terminal condition based on time, independent of step length.""" + + def __init__(self, max_time_s: float): + """Initializes the MaxTimeTerminalCondition. + + Args: + max_time_s: Time limit in seconds. In sim, this is the simulation time, + not wall time. + """ + if max_time_s <= 0: + raise ValueError("Max time for MaxTimeTerminalCondition cannot be zero " + "or less. Input value: %s" % max_time_s) + self._max_time_s = max_time_s + + def __call__(self, env): + if self._max_time_s is None: + return False, None + is_done = env.get_time_since_reset() >= self._max_time_s + term_reason = tr.TerminationReason.RUN_TIME_LIMIT if is_done else None + return is_done, term_reason + + +@gin.configurable +class MovementDetectorTerminalCondition(object): + """Terminal condition for not moving past a distance in specified time.""" + + def __init__(self, + max_time_s: float = None, + min_travel_distance_m: float = 1.0): + """Initializes the MovementDetectorTerminalCondition. + + Args: + max_time_s: Time limit in seconds. In sim, this is the simulation time, + not wall time. + min_travel_distance_m: Minimum distance in meters to travel in time limit. + """ + if max_time_s is not None and max_time_s <= 0: + raise ValueError("Max time for MovementDetectorTerminalCondition cannot " + "be zero or less. Input value: %s" % max_time_s) + if min_travel_distance_m is not None and min_travel_distance_m < 0: + raise ValueError( + "Minimum travel distance for MovementDetectorTerminalCondition " + "cannot be less than zero. Input value: %s" % min_travel_distance_m) + self._max_time_s = max_time_s + self._min_travel_distance_m = min_travel_distance_m + self._not_advancing_limit = None + self._reference_position = None + + def _update_limit_time(self, env): + self._not_advancing_limit = env.get_time_since_reset() + self._max_time_s + + def _current_position(self, env): + return np.asarray(env.robot.base_position[:2]) + + def _exceed_time_limit(self, env): + return self._not_advancing_limit < env.get_time_since_reset() + + def __call__(self, env): + is_done = False + term_reason = None + + if self._max_time_s is None: + return is_done, term_reason + + if self._not_advancing_limit is None or self._reference_position is None: + self._update_limit_time(env) + self._reference_position = self._current_position(env) + + distance_shifted = np.linalg.norm( + self._current_position(env) - self._reference_position) + if distance_shifted >= self._min_travel_distance_m: + self._update_limit_time(env) + self._reference_position = self._current_position(env) + + if self._exceed_time_limit(env): + is_done = True + term_reason = tr.TerminationReason.NOT_ADVANCING_LIMIT + + return is_done, term_reason + + + +@gin.configurable +def bad_front_leg_terminal_condition(env, max_angle=0.8): + """A terminal condition for checking whether front legs are bent backward. + + Args: + env: An instance of MinitaurGymEnv + max_angle: The maximum angle allowed for front legs + + Returns: + A boolean indicating if front legs are bent backward or not + """ + motor_angles = env.robot.motor_angles + leg_pose = minitaur_pose_utils.motor_angles_to_leg_pose(motor_angles) + swing0 = leg_pose[0] + swing1 = leg_pose[2] + return swing0 > max_angle or swing1 > max_angle + + +@gin.configurable +def logical_any_terminal_condition(env, conditions): + """A logical "Any" operator for terminal conditions. + + Args: + env: An instance of MinitaurGymEnv + conditions: a list of terminal conditions + + Returns: + A boolean indicating if any of terminal conditions is satisfied + """ + return any([cond(env) for cond in conditions]) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils.py new file mode 100644 index 000000000..f82283bf3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils.py @@ -0,0 +1,175 @@ +"""Utility functions to manipulate environment.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import gin +from gym import spaces +import numpy as np + +import tensorflow.compat.v1 as tf + + + + +def flatten_observations(observation_dict, observation_excluded=()): + """Flattens the observation dictionary to an array. + + If observation_excluded is passed in, it will still return a dictionary, + which includes all the (key, observation_dict[key]) in observation_excluded, + and ('other': the flattened array). + + Args: + observation_dict: A dictionary of all the observations. + observation_excluded: A list/tuple of all the keys of the observations to be + ignored during flattening. + + Returns: + An array or a dictionary of observations based on whether + observation_excluded is empty. + """ + if not isinstance(observation_excluded, (list, tuple)): + observation_excluded = [observation_excluded] + observations = [] + for key, value in observation_dict.items(): + if key not in observation_excluded: + observations.append(np.asarray(value).flatten()) + flat_observations = np.float32(np.concatenate(observations)) + if not observation_excluded: + return flat_observations + else: + observation_dict_after_flatten = {"other": flat_observations} + for key in observation_excluded: + if key in observation_dict: + observation_dict_after_flatten[key] = observation_dict[key] + return collections.OrderedDict( + sorted(list(observation_dict_after_flatten.items()))) + + +def flatten_observation_dim(observation_dim, observation_excluded=()): + """Flattens the observation dimensions to an array. + + If observation_excluded is passed in, it will still return a dictionary, + which includes all the (key, observation_dict[key]) in observation_excluded, + and ('other': the flattened array). + + Args: + observation_dim: A dictionary of all the observation dimensions. + observation_excluded: A list/tuple of all the keys of the observations to be + ignored during flattening. + + Returns: + An array or a dictionary of observation dimensions based on whether + observation_excluded is empty. + """ + if not isinstance(observation_excluded, (list, tuple)): + observation_excluded = [observation_excluded] + observation_dims = 0 + for key, value in observation_dim.items(): + if key not in observation_excluded: + observation_dims += value + if not observation_excluded: + return observation_dims + else: + dim_dict_after_flatten = {"other": observation_dims} + for key in observation_excluded: + if key in observation_dim: + dim_dict_after_flatten[key] = observation_dim[key] + return collections.OrderedDict(sorted(list(dim_dict_after_flatten.items()))) + + +def flatten_observation_spaces(observation_spaces, observation_excluded=()): + """Flattens the dictionary observation spaces to gym.spaces.Box. + + If observation_excluded is passed in, it will still return a dictionary, + which includes all the (key, observation_spaces[key]) in observation_excluded, + and ('other': the flattened Box space). + + Args: + observation_spaces: A dictionary of all the observation spaces. + observation_excluded: A list/tuple of all the keys of the observations to be + ignored during flattening. + + Returns: + A box space or a dictionary of observation spaces based on whether + observation_excluded is empty. + """ + if not isinstance(observation_excluded, (list, tuple)): + observation_excluded = [observation_excluded] + lower_bound = [] + upper_bound = [] + for key, value in observation_spaces.spaces.items(): + if key not in observation_excluded: + lower_bound.append(np.asarray(value.low).flatten()) + upper_bound.append(np.asarray(value.high).flatten()) + lower_bound = np.concatenate(lower_bound) + upper_bound = np.concatenate(upper_bound) + observation_space = spaces.Box( + np.array(lower_bound), np.array(upper_bound), dtype=np.float32) + if not observation_excluded: + return observation_space + else: + observation_spaces_after_flatten = {"other": observation_space} + for key in observation_excluded: + if key in observation_spaces.spaces: + observation_spaces_after_flatten[key] = observation_spaces[key] + return spaces.Dict(observation_spaces_after_flatten) + + + +@gin.configurable +def get_action_spec(action_spec): + """Get action spec for one agent from the environment specs.""" + return list(action_spec.values())[0] + + + +@gin.configurable +def get_get_actions_fn(agent_name_to_index): + """Get function which returns other agents' actions.""" + + def get_actions(action): + """Returns a list of actions. + + Args: + action: A dictionary of action tensors with keys matching agent names + + Returns: + critic_actions: A list of action tensors for (N-1) other agents. + Shape: (B x N, N-1, D) + """ + critic_actions = [] + for agent_name in sorted(agent_name_to_index.keys()): + other_agent_actions = [] + for other_agent_name in sorted(agent_name_to_index.keys()): + if other_agent_name != agent_name: + other_agent_actions.append(action[other_agent_name]) + critic_actions.append(other_agent_actions) + print([tf.shape(critic_action) for critic_action in critic_actions]) + # Shape goes from (N, N-1, B, D) to (B x N, N-1, D) + critic_actions = tf.transpose(tf.concat(critic_actions, axis=1), (1, 0, 2)) + return critic_actions + + return get_actions + + + + +def get_robot_base_position(robot): + """Gets the base position of robot.""" + # TODO(b/151975607): Clean this after robot interface migration. + if hasattr(robot, "GetBasePosition"): + return robot.GetBasePosition() + else: + return robot.base_position + + +def get_robot_base_orientation(robot): + """Gets the base orientation of robot.""" + # TODO(b/151975607): Clean this after robot interface migration. + if hasattr(robot, "GetBaseOrientation"): + return robot.GetBaseOrientation() + else: + return robot.base_orientation_quaternion diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils_v2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils_v2.py new file mode 100644 index 000000000..02d2eeaef --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/env_utils_v2.py @@ -0,0 +1,22 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +def get_robot_base_position(robot): + """Gets the base position of robot.""" + # TODO(b/151975607): Clean this after robot interface migration. + if hasattr(robot, "GetBasePosition"): + return robot.GetBasePosition() + else: + return robot.base_position + + +def get_robot_base_orientation(robot): + """Gets the base orientation of robot.""" + # TODO(b/151975607): Clean this after robot interface migration. + if hasattr(robot, "GetBaseOrientation"): + return robot.GetBaseOrientation() + else: + return robot.base_orientation_quaternion \ No newline at end of file diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/laikago_pose_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/laikago_pose_utils.py new file mode 100644 index 000000000..f328c3418 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/laikago_pose_utils.py @@ -0,0 +1,50 @@ +# coding=utf-8 +# Copyright 2020 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions to calculate Laikago's pose and motor angles.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import attr + +LAIKAGO_DEFAULT_ABDUCTION_ANGLE = 0 +LAIKAGO_DEFAULT_HIP_ANGLE = 0.67 +LAIKAGO_DEFAULT_KNEE_ANGLE = -1.25 + + +@attr.s +class LaikagoPose(object): + """Default pose of the Laikago. + + Leg order: + 0 -> Front Right. + 1 -> Front Left. + 2 -> Rear Right. + 3 -> Rear Left. + """ + abduction_angle_0 = attr.ib(type=float, default=0) + hip_angle_0 = attr.ib(type=float, default=0) + knee_angle_0 = attr.ib(type=float, default=0) + abduction_angle_1 = attr.ib(type=float, default=0) + hip_angle_1 = attr.ib(type=float, default=0) + knee_angle_1 = attr.ib(type=float, default=0) + abduction_angle_2 = attr.ib(type=float, default=0) + hip_angle_2 = attr.ib(type=float, default=0) + knee_angle_2 = attr.ib(type=float, default=0) + abduction_angle_3 = attr.ib(type=float, default=0) + hip_angle_3 = attr.ib(type=float, default=0) + knee_angle_3 = attr.ib(type=float, default=0) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/mini_cheetah_pose_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/mini_cheetah_pose_utils.py new file mode 100644 index 000000000..37d12fe95 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/mini_cheetah_pose_utils.py @@ -0,0 +1,35 @@ +"""Utility functions to calculate Mini Cheetah's pose and motor angles.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import attr + +MINI_CHEETAH_DEFAULT_ABDUCTION_ANGLE = 0 +MINI_CHEETAH_DEFAULT_HIP_ANGLE = -1.2 +MINI_CHEETAH_DEFAULT_KNEE_ANGLE = 2.1 + + +@attr.s +class MiniCheetahPose(object): + """Default pose of the Laikago. + + Leg order: + 0 -> Front Right. + 1 -> Front Left. + 2 -> Rear Right. + 3 -> Rear Left. + """ + abduction_angle_0 = attr.ib(type=float, default=0) + hip_angle_0 = attr.ib(type=float, default=0) + knee_angle_0 = attr.ib(type=float, default=0) + abduction_angle_1 = attr.ib(type=float, default=0) + hip_angle_1 = attr.ib(type=float, default=0) + knee_angle_1 = attr.ib(type=float, default=0) + abduction_angle_2 = attr.ib(type=float, default=0) + hip_angle_2 = attr.ib(type=float, default=0) + knee_angle_2 = attr.ib(type=float, default=0) + abduction_angle_3 = attr.ib(type=float, default=0) + hip_angle_3 = attr.ib(type=float, default=0) + knee_angle_3 = attr.ib(type=float, default=0) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/minitaur_pose_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/minitaur_pose_utils.py new file mode 100644 index 000000000..34ad9da01 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/minitaur_pose_utils.py @@ -0,0 +1,187 @@ +# coding=utf-8 +# Copyright 2020 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions to calculate Minitaur's pose and motor angles.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import attr +import numpy as np + +NUM_MOTORS = 8 +NUM_LEGS = 4 +MOTOR_SIGNS = (1, 1, -1, -1) +# Constants for the function swing_extend_to_motor_angles +EPS = 0.1 +# Range of motion for the legs (does not allow pointing towards the body). +LEG_SWING_LIMIT_LOW = -math.pi / 2 + EPS +LEG_SWING_LIMIT_HIGH = 3 * math.pi / 2 - EPS +# Range of gap between motors for feasibility. +MOTORS_GAP_LIMIT_HIGH = 2 * math.pi - EPS +MOTORS_GAP_LIMIT_LOW = EPS + + +@attr.s +class MinitaurPose(object): + """Default pose of the Minitaur.""" + swing_angle_0 = attr.ib(type=float, default=0) + swing_angle_1 = attr.ib(type=float, default=0) + swing_angle_2 = attr.ib(type=float, default=0) + swing_angle_3 = attr.ib(type=float, default=0) + extension_angle_0 = attr.ib(type=float, default=0) + extension_angle_1 = attr.ib(type=float, default=0) + extension_angle_2 = attr.ib(type=float, default=0) + extension_angle_3 = attr.ib(type=float, default=0) + + +def motor_angles_to_leg_pose(motor_angles): + """Convert motor angles to the leg pose. + + A single leg pose is a tuple (swing, extension). The definition can be find + in: + Sim-to-Real: Learning Agile Locomotion For Quadruped Robot + + Args: + motor_angles: A numpy array. Contains all eight motor angles for Minitaur. + + Returns: + A numpy array. Contains the leg pose for all four legs: [swing_0, swing_1, + swing_2, swing_3, extension_0, extension_1, extension_2, extension_3] + + """ + motor_angles = np.array(motor_angles) + + swings = 0.5 * np.multiply( + np.array(MOTOR_SIGNS), (motor_angles[1::2] - motor_angles[::2])) + extensions = 0.5 * (motor_angles[::2] + motor_angles[1::2]) + + return np.concatenate((swings, extensions), axis=None) + + +def leg_pose_to_motor_angles(leg_pose): + """Converts the leg pose to the motor angles. + + Args: + leg_pose: A numpy array. Contains the leg pose for all four legs: [swing_0, + swing_1, swing_2, swing_3, extension_0, extension_1, extension_2, + extension_3] + + Returns: + A numpy array. All eight motor angles. + """ + leg_pose = np.array(leg_pose) + + # swings multiplied with the sign array. + signed_swings = np.multiply(np.array(MOTOR_SIGNS), leg_pose[0:NUM_LEGS]) + extensions = leg_pose[NUM_LEGS:] + + motor_angles = np.zeros(NUM_MOTORS) + motor_angles[1::2] = signed_swings + extensions + motor_angles[::2] = extensions - signed_swings + return motor_angles + + +# This method also does the same conversion, but 0 swing and 0 extension maps +# to a neutral standing still motor positions with motors at + or - pi. It also +# contains a safety layer so that the legs don't swing or extend too much to hit +# the body of the robot. +def leg_pose_to_motor_angles_with_half_pi_offset_and_safety(leg_pose): + """Converts the swing extension poses to the motor angles with safety limits. + + Args: + leg_pose: A numpy array. Contains the leg pose for all four legs: [swing_0, + extension_0, swing_1, extension_1, swing_2, extension_2, swing_3, + extension_3] + + Returns: + A numpy array. All eight motor angles. + """ + + motor_angles = [] + for idx in range(4): + swing = leg_pose[idx * 2] + extend = leg_pose[idx * 2 + 1] + motor_angles.extend(swing_extend_to_motor_angles(idx, swing, extend)) + return motor_angles + + +def swing_extend_to_motor_angles(leg_id, swing, extension, noise_stdev=0): + """Swing - extension based leg model for minitaur. + + Swing extension leg model calculates motor positions using 2 separate motions: + swing and extension. Swing rotates the whole leg by rotating both motors + equally towards same direction. Extension increases or decreases the length + of the leg by turning both motors equally in opposite direction. + + This method also does the same conversion as leg_pose_to_motor_angles, but 0 + swing and 0 extension maps to a neutral standing still motor positions with + motors at + or - pi. + Args: + leg_id: The id of the leg that the conversion is made for (0, 1, 2, 3). + swing: Swing degree for the leg (in radians). 0 means perpendicular to the + body). + extension: Extension level (length) of the leg, limited to [-1, 1]. + noise_stdev: Standard deviation of the introduced noise at the motor + position level. Noise is turned off by default. + + Returns: + motor0: Position for the first motor for that leg. + motor1: Position for the second motor for that leg. + Raises: + ValueError: In case calculated positions are outside the allowed boundaries. + """ + # Check if the leg_id is in valid range + if not 0 <= leg_id <= 3: + raise ValueError('leg {} does not exist for a quadruped.'.format(leg_id)) + + # Front legs can not swing too much towards the body. + if leg_id % 2 == 0: + swing = np.clip(swing, LEG_SWING_LIMIT_LOW, LEG_SWING_LIMIT_HIGH) + # Back legs can not swing too much towards the body (opposite direction). + else: + swing = np.clip(swing, -LEG_SWING_LIMIT_HIGH, -LEG_SWING_LIMIT_LOW) + + # Check if the motors are too close or too far away to make it impossible + # for the physical robot. + gap = math.pi - 2 * extension + if gap < MOTORS_GAP_LIMIT_LOW or gap > MOTORS_GAP_LIMIT_HIGH: + top_extension = (math.pi - MOTORS_GAP_LIMIT_LOW) / 2.0 + least_extension = (math.pi - MOTORS_GAP_LIMIT_HIGH) / 2.0 + extension = np.clip(extension, least_extension, top_extension) + + # Initialization to neutral standing position where both motors point to + # opposite directions + motor0 = math.pi / 2 + motor1 = math.pi / 2 + # Rotational move + if leg_id in (0, 1): + motor0 += swing + motor1 -= swing + elif leg_id in (2, 3): + motor0 -= swing + motor1 += swing + # Extension + motor0 += extension + motor1 += extension + + # Add noise if requested. + if noise_stdev > 0: + motor0 += np.random.normal(0, noise_stdev) + motor1 += np.random.normal(0, noise_stdev) + + return motor0, motor1 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/noise_generators.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/noise_generators.py new file mode 100644 index 000000000..012a93421 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/noise_generators.py @@ -0,0 +1,255 @@ +"""Noise generators to simulate noise in sensor / actuator classes.""" + +import abc +import gin +import numpy as np + + +class NoiseGenerator(metaclass=abc.ABCMeta): + """Base class for noise generators.""" + + @abc.abstractmethod + def _get_noise(self, shape, dtype=None): + """Gets noise as a numpy array in the specified shape and dtype. + + Tensorflow requires the shape and dtype of noise to be correctly specified, + so the generator needs to know this to produce data of the correct type. + + Args: + shape: Shape of the returned array. + dtype: Datatype of returned array (None for default). + """ + + @abc.abstractmethod + def add_noise(self, data): + """Adds noise generated by _get_noise to the given data with clipping. + + Args: + data: Numpy array of data to be modified with noise. + """ + + +@gin.configurable +class BiasNoise(NoiseGenerator): + """Adds bias to the data, possibly with clipping.""" + + def __init__(self, + bias=0.0, + clipping_lower_bound=-np.inf, + clipping_upper_bound=np.inf): + """Create a bias noise generator. + + Args: + bias: Absolute magnitude of bias applied to input. + clipping_lower_bound: lower bound of add_noise (use -np.inf to ignore). + clipping_upper_bound: Upper bound of add_noise (use np.inf to ignore). + """ + self._bias = bias + self._clipping_lower_bound = clipping_lower_bound + self._clipping_upper_bound = clipping_upper_bound + + def _get_noise(self, shape, dtype=None): + """Create bias noise of the given direction and datatype.""" + return np.full(shape, self._bias, dtype) + + def add_noise(self, data): + """Add bias noise to the given data, clipping to the given range.""" + noise = self._get_noise(data.shape, data.dtype) + return np.clip(data + noise, self._clipping_lower_bound, + self._clipping_upper_bound) + + +@gin.configurable +class NormalNoise(BiasNoise): + """Adds Gaussian noise to the data, possibly with clipping.""" + + def __init__(self, scale, **kwargs): + """Create a normal noise generator. + + Args: + scale: Absolute magnitude of standard deviation of Gaussian noise. Note + numpy will throw an error if scale < 0. + **kwargs: Arguments passed to BiasNoise (e.g. bias and clipping). + """ + super(NormalNoise, self).__init__(**kwargs) + self._scale = scale + + def _get_noise(self, shape, dtype=None): + """Create normal noise of the given direction and datatype.""" + return np.random.normal(self._bias, self._scale, shape).astype(dtype) + + +@gin.configurable +class UniformNoise(NoiseGenerator): + """Generates uniform noise in the given range.""" + + def __init__(self, + low, + high, + clipping_lower_bound=-np.inf, + clipping_upper_bound=np.inf): + """Creates a uniform noise generator. + + Args: + low: the lower bound of the noise. + high: the higher bound of the noise. + clipping_lower_bound: lower bound of add_noise (use -np.inf to ignore). + clipping_upper_bound: Upper bound of add_noise (use np.inf to ignore). + """ + super().__init__() + self._low = low + self._high = high + self._clipping_lower_bound = clipping_lower_bound + self._clipping_upper_bound = clipping_upper_bound + + def _get_noise(self, shape, dtype=None): + """Generates a noise using the given shape and data type.""" + return np.random.uniform(self._low, self._high, shape).astype(dtype) + + def add_noise(self, data): + """Adds noise to the given data, clipping to the given bound.""" + noise = self._get_noise(data.shape, data.dtype) + return np.clip(data + noise, self._clipping_lower_bound, + self._clipping_upper_bound) + + +@gin.configurable +class RangeNoise(NormalNoise): + """Add normally distributed noise in m, applied to hit fractions in (0, 1). + + This enables us to specify range noise in terms of meters of Gaussian noise + between a maximum and minimum range, but the add_noise is applied as above + to values expected to be in a hit fraction range of (0, 1) as needed for the + SimLidarSensor API. Separate methods return noise or noisify data in meters. + """ + + def __init__(self, range_noise_m, max_range_m, min_range_m=0.0, **kwargs): + """Create a normal noise generator suitable for use in a range scanner. + + Args: + range_noise_m: Absolute magnitude of standard deviation of Gaussian noise, + applied to range observation readngs, measured in meters. + max_range_m: Maximum range in meters of the data, used for clipping. + min_range_m: Minimum range in meters of the data, used for clipping. + **kwargs: Other arguments passed to NormalNoise (principally bias). + """ + # Validate range values. + if range_noise_m < 0.0: + raise ValueError("Range noise should not be negative: %r" % range_noise_m) + if min_range_m >= max_range_m: + raise ValueError("min_range_m %s must be less than max_range_m %s" % + (min_range_m, max_range_m)) + + self._range_noise_m = range_noise_m + self._max_range_m = max_range_m + self._min_range_m = min_range_m + self._total_range = max_range_m - min_range_m + super(RangeNoise, self).__init__( + scale=range_noise_m / self._total_range, + clipping_lower_bound=0.0, + clipping_upper_bound=1.0, + **kwargs) + + def _get_noise_m(self, shape, dtype=None): + """Create normal noise of the given direction and datatype, in meters.""" + return self.range_to_m(self._get_noise(shape=shape, dtype=dtype)) + + def add_noise_m(self, data): + """Add normal noise to the given data, scaled in meters.""" + return self.range_to_m(self.add_noise(self.m_to_range(data))) + + def m_to_range(self, data): + """Scale data in meters to a range of (0, 1).""" + return (data - self._min_range_m) / self._total_range + + def range_to_m(self, data): + """Scale data in range of (0, 1) to meters.""" + return data * self._total_range + self._min_range_m + + +@gin.configurable +class TwistNoise(object): + """Add normally distributed noise to twist actions. + + Note this is a simplified noise model in action space designed for parity + with DriveWorld's r/s/e/drive_models/twist_drive.py;rcl=307540784;l=161. + This assumes that the TwistNoise will be applied to a twist action which is + then clipped, as currently done in wheeled_robot_base.py: + + robotics/reinforcement_learning/minitaur/robots/wheeled_robot_base.py;l=533 + # We assume that the velocity clipping would be absorbed in this API. + if self._action_filter: + action = self._action_filter.filter(action) + + where action is a linear_velocity, angular_velocity pair, which is clipped + to limits subsequently by the _compute_kinematic_base_velocity method. + """ + + def __init__(self, + linear_velocity_noise_stdev_mps: float, + linear_velocity_noise_max_stdevs: float, + angular_velocity_noise_stdev_rps: float, + angular_velocity_noise_max_stdevs: float, + noise_scaling_cutoff_mps: float = 0.0): + """Create a normal noise generator suitable for use in a range scanner. + + Supports the API specified in the DriveWorld TwistDrive class: + robotics/simulation/environments/drive_models/twist_drive.py;l=54 + + Args: + linear_velocity_noise_stdev_mps: One standard deviation of normal noise + for linear velocity in meters per second. + linear_velocity_noise_max_stdevs: Max stdevs for linear velocity noise. + This ensures that the noise values do not spike too crazy. + angular_velocity_noise_stdev_rps: One standard deviation of normal noise + for angular velocity in radians per second. + angular_velocity_noise_max_stdevs: Max stdevs for angular velocity noise. + noise_scaling_cutoff_mps: If linear velocity is less than this cutoff, + linear and angular noise are scaled so that zero velocity produces zero + noise. This enables a robot at rest to remain at rest, while still + applying reasonable noise values to finite velocities. Angular velocity + does not contribute to this computation to keep the model simple. + """ + # Validate range values. + if linear_velocity_noise_stdev_mps < 0.0: + raise ValueError("Linear action noise should not be negative: %r" % + linear_velocity_noise_stdev_mps) + if linear_velocity_noise_max_stdevs < 0.0: + raise ValueError("Maximum linear noise should not be negative: %r" % + linear_velocity_noise_max_stdevs) + if angular_velocity_noise_stdev_rps < 0.0: + raise ValueError("Angular action noise should not be negative: %r" % + angular_velocity_noise_stdev_rps) + if angular_velocity_noise_max_stdevs < 0.0: + raise ValueError("Maximum action noise should not be negative: %r" % + angular_velocity_noise_max_stdevs) + if noise_scaling_cutoff_mps < 0.0: + raise ValueError("Noise scaling cutoff should not be negative: %r" % + noise_scaling_cutoff_mps) + + # Save the values to create our noise later. + self._noise_shape = [ + linear_velocity_noise_stdev_mps, angular_velocity_noise_stdev_rps + ] + # The noise clipping is performed using one standard deviation as the unit. + self._noise_lower_bound = np.array([ + -linear_velocity_noise_max_stdevs * linear_velocity_noise_stdev_mps, + -angular_velocity_noise_max_stdevs * angular_velocity_noise_stdev_rps + ]) + self._noise_upper_bound = -self._noise_lower_bound + self._noise_scaling_cutoff_mps = noise_scaling_cutoff_mps + + def filter(self, action): + """Filter the linear and angular velocity by adding noise.""" + linear_velocity, angular_velocity = action + linear_noise, angular_noise = np.clip( + np.random.normal(0, self._noise_shape, 2), self._noise_lower_bound, + self._noise_upper_bound) + if self._noise_scaling_cutoff_mps: + clipped_velocity = min(abs(linear_velocity), + self._noise_scaling_cutoff_mps) + scaling_factor = clipped_velocity / self._noise_scaling_cutoff_mps + linear_noise *= scaling_factor + angular_noise *= scaling_factor + + return (linear_velocity + linear_noise, angular_velocity + angular_noise) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/rendering_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/rendering_utils.py new file mode 100644 index 000000000..99568108b --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/rendering_utils.py @@ -0,0 +1,272 @@ +"""Rendering utilities.""" + +import enum +import math +import os +from typing import Any, Callable, Dict, Iterable, Optional, Text +from absl import logging +import gin +import numpy as np + + +# These matrices will change by any call to render() and should be saved +# immediately to the local object. +last_used_view_matrix = None +last_used_proj_matrix = None +last_rendered_image_size = None + +# Bounds of plane loaded with GEOM_PLANE. +_INFINITY = 1.0e30 +# In case of infinite plane, use this scene bounding box. +_DEFAULT_BOUNDING_BOX = ((-15, -15, -10), (15, 15, 0)) + + +def render_image(pybullet_client, width, height, view_matrix, proj_matrix): + """Renders image as numpy array given view and projection matrices.""" + global last_used_view_matrix, last_used_proj_matrix, last_rendered_image_size + last_used_view_matrix = view_matrix + last_used_proj_matrix = proj_matrix + + (_, _, px, _, _) = pybullet_client.getCameraImage( + width=width, + height=height, + renderer=pybullet_client.ER_BULLET_HARDWARE_OPENGL, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix) + rgb_array = np.array(px) + image = rgb_array[:, :, :3] + last_rendered_image_size = (image.shape[1], image.shape[0]) + return image + + +def project_world_to_image(points, + view_matrix=None, + proj_matrix=None, + image_size=None, + include_z_coord=False): + """Projects 3D world-space points to 2D or 3D image-space points. + + If no projection matrices or image_size are given, the last ones are used. + This means that you can render an image with `render_image` function (or other + functions that use this one) and then project points to that image immediately + after that call. Otherwise, you can save `last_used_view_matrix` and + `last_used_proj_matrix` variables and pass them in later. + + Args: + points: Sequence of 3D points. + view_matrix: Optional view matrix (column-major). + proj_matrix: Optional projection matrix (column-major). + image_size: Optional image size for resulting coords. + include_z_coord: Whether to include Z coordinate in the resulting + projection. Z coordinate goes from 0 (far plane) to 1 (near plane). + + Returns: + A Numpy array of shape (points_count, 2) or (points_count, 3) when + `include_z_coord` is True, with dtype of np.float32. + """ + # Note that these matrices are column-major. + view_matrix = np.array( + view_matrix or last_used_view_matrix, dtype=np.float32).reshape((4, 4)) + proj_matrix = np.array( + proj_matrix or last_used_proj_matrix, dtype=np.float32).reshape((4, 4)) + mvp_matrix = np.matmul(view_matrix, proj_matrix) + + # Add w component equal to 1 (for perspective projection). + points = np.asarray(points, dtype=np.float32) + points_4d = np.concatenate( + [points, np.ones((points.shape[0], 1), dtype=np.float32)], axis=-1) + points_proj = np.matmul(points_4d, mvp_matrix) + + if include_z_coord: + # Perspective divide (only keep X, Y, and Z, discard W). + points_proj_3d = points_proj[:, 0:3] / np.expand_dims(points_proj[:, 3], -1) + # Shift origin to bottom left and rescale range to [0,1]. This assumes + # OpenGL projection space. + points_proj_3d = (points_proj_3d + 1) * 0.5 + # Invert y-axis to have a coordinate with (0,0) on the top left. + points_proj_3d[:, 1] = 1 - points_proj_3d[:, 1] + # Scale projection to image size, ignore Z. + image_size = np.asarray( + image_size or last_rendered_image_size, dtype=np.float32) + image_size = np.append(image_size, 1) + return points_proj_3d * image_size + else: + # Perspective divide (only keep X and Y, discard Z and W). + points_proj_2d = points_proj[:, 0:2] / np.expand_dims(points_proj[:, 3], -1) + # Shift origin to bottom left and rescale range to [0,1]. This assumes + # OpenGL projection space. + points_proj_2d = (points_proj_2d + 1) * 0.5 + # Invert y-axis to have a coordinate with (0,0) on the top left. + points_proj_2d[:, 1] = 1 - points_proj_2d[:, 1] + # Scale projection to image size. + image_size = np.asarray( + image_size or last_rendered_image_size, dtype=np.float32) + return points_proj_2d * image_size + + +def get_scene_bounding_box(pybullet_client, scene=None): + """Computes scene axis-aligned bounding box. + + Args: + pybullet_client: PyBullet client. + scene: Scene instance for filtering the bounding box of camera. + + Returns: + A tuple of min and max 3D coordinates. Returns (None, None) if the scene + is empty. + """ + aabb_min = None + aabb_max = None + + for i in range(pybullet_client.getNumBodies()): + body_id = pybullet_client.getBodyUniqueId(i) + + # If a scene has been provided, only count bodes which are in + # either the ground or obstacle id lists. + if scene is not None: + if body_id not in scene.ground_ids and body_id not in scene.obstacle_ids: + continue + + aabb = pybullet_client.getAABB(body_id) + if np.any(np.abs(aabb) >= _INFINITY): + aabb = _DEFAULT_BOUNDING_BOX + if aabb_min is None: + aabb_min = aabb[0] + aabb_max = aabb[1] + else: + aabb_min = np.minimum(aabb_min, aabb[0]) + aabb_max = np.maximum(aabb_max, aabb[1]) + + return aabb_min, aabb_max + + +@gin.configurable +def render_topdown( + pybullet_client, + result_size=(1280, 720), + scale_px_per_meter=None, + camera_height=50, + ground_height=None, + low_render_height_from_ground=None, + high_render_height_from_ground=None, + scene=None, + use_y_as_up_axis=False, + rendered_origin_and_size=None, +): + """Renders top-down image of the environment. + + Args: + pybullet_client: PyBullet client. + result_size: Resulting image size. + scale_px_per_meter: Resulting image scale in pixels per meter. This + overrides `result_size`. + camera_height: Height of the camera above the environment. This is not very + significant, the lower the height, the larger perspective distortion. + ground_height: Ground height for following two parameters. + low_render_height_from_ground: If set, rendering is cut below this distance + from ground. + high_render_height_from_ground: If set, rendering is cut above this + distance from ground. + scene: Scene instance for filtering the bounding box of camera. + use_y_as_up_axis: Whether to consider Y axis as world's "up" instead of Z. + rendered_origin_and_size: If set, sets rendered origin (bounding box min) + and size (bounding box size) instead of computing that from the scene. + Expected format: two tuples of [x, y z] coords containing origin and size + of rendered bounding box. + + Returns: + RGB image as 3D numpy array. + """ + # Get bounds of the current environment. + if rendered_origin_and_size is not None: + aabb_min, aabb_size = rendered_origin_and_size + if len(aabb_min) != 3: + raise ValueError( + "Invalid render origin, expected [x, y, z]: {}".format(aabb_min)) + if len(aabb_size) != 3: + raise ValueError( + "Invalid render size, expected [x, y, z]: {}".format(aabb_size)) + aabb_max = tuple(m + s for m, s in zip(aabb_min, aabb_size)) + else: + aabb_min, aabb_max = get_scene_bounding_box(pybullet_client, scene) + + if use_y_as_up_axis: + width = aabb_max[0] - aabb_min[0] + height = aabb_max[2] - aabb_min[2] + z_size = aabb_max[1] - aabb_min[1] + z_max = aabb_max[1] + else: + width = aabb_max[0] - aabb_min[0] + height = aabb_max[1] - aabb_min[1] + z_size = aabb_max[2] - aabb_min[2] + z_max = aabb_max[2] + + if scale_px_per_meter is not None: + result_size = (int(width * scale_px_per_meter), + int(height * scale_px_per_meter)) + else: + # Adjust scene size to fit inside of the given result size. + if len(result_size) != 2 or result_size[0] <= 0 or result_size[1] <= 0: + raise ValueError("Invalid result size: {}".format(result_size)) + img_width, img_height = result_size + if img_width / width < img_height / height: + # Width is limiting. Adjust height to match. + adjusted_height = img_height * width / img_width + assert adjusted_height >= height, (adjusted_height, height) + height = adjusted_height + else: + # Height is limiting. Adjust width to match. + adjusted_width = img_width * height / img_height + assert adjusted_width >= width, (adjusted_width, width) + width = adjusted_width + + # Compute view and projection matrices. + if use_y_as_up_axis: + center_x = (aabb_min[0] + aabb_max[0]) / 2.0 + center_z = (aabb_min[2] + aabb_max[2]) / 2.0 + view_matrix = pybullet_client.computeViewMatrix( + cameraEyePosition=(center_x, aabb_max[1] + camera_height, center_z), + cameraTargetPosition=(center_x, aabb_max[1], center_z), + cameraUpVector=(0, 0, 1)) + else: + center_x = (aabb_min[0] + aabb_max[0]) / 2.0 + center_y = (aabb_min[1] + aabb_max[1]) / 2.0 + view_matrix = pybullet_client.computeViewMatrix( + cameraEyePosition=(center_x, center_y, aabb_max[2] + camera_height), + cameraTargetPosition=(center_x, center_y, aabb_max[2]), + cameraUpVector=(0, 1, 0)) + + near_plane = camera_height + far_plane = camera_height + z_size + if ground_height is not None: + if low_render_height_from_ground is not None: + far_plane = ( + camera_height + z_max - ground_height - low_render_height_from_ground) + if high_render_height_from_ground is not None: + near_plane = ( + camera_height + z_max - ground_height - + high_render_height_from_ground) + else: + if (low_render_height_from_ground is not None or + high_render_height_from_ground is not None): + raise ValueError( + "The `low_render_height_from_ground` or " + "`high_render_height_from_ground` were specified but no reference " + "ground height was given.") + vertical_fov = 2 * math.atan2(height / 2, camera_height) + proj_matrix = pybullet_client.computeProjectionMatrixFOV( + fov=math.degrees(vertical_fov), + aspect=width / height, + nearVal=near_plane, + farVal=far_plane) + + # Render and return image. + return render_image( + pybullet_client, + result_size[0], + result_size[1], + view_matrix, + proj_matrix, + ) + + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/robot_pose_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/robot_pose_utils.py new file mode 100644 index 000000000..6e0e92243 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/robot_pose_utils.py @@ -0,0 +1,189 @@ +"""This file implements the robot specific pose tools.""" +import math + +import attr +import numpy as np + +from pybullet_envs.minitaur.envs_v2.utilities import laikago_pose_utils +from pybullet_envs.minitaur.envs_v2.utilities import mini_cheetah_pose_utils +from pybullet_envs.minitaur.envs_v2.utilities import minitaur_pose_utils +from pybullet_envs.minitaur.robots import laikago +from pybullet_envs.minitaur.robots import laikago_v2 +from pybullet_envs.minitaur.robots import mini_cheetah +from pybullet_envs.minitaur.robots import minitaur_v2 + +_ABDUCTION_ACTION_INDEXES = [0, 3, 6, 9] + +# The default values used to give a neutral pose for minitaur. +_MINITAUR_DEFAULT_EXTENSION_POS = math.pi / 2 +_MINITAUR_DEFAULT_SWING_POS = 0 + +_LAIKAGO_NEUTRAL_POSE_HIP_ANGLE = math.pi / 4 +_LAIKAGO_NEUTRAL_POSE_KNEE_ANGLE = -math.pi / 2 +_LAIKAGO_EXTENSION_CONVERSION_MULTIPLIER = 1.0 +_LAIKAGO_SWING_CONVERSION_MULTIPLIER = -1.0 + +_MINI_CHEETAH_NEUTRAL_POSE_HIP_ANGLE = -math.pi / 4 +_MINI_CHEETAH_NEUTRAL_POSE_KNEE_ANGLE = math.pi / 2 +_MINI_CHEETAH_EXTENSION_CONVERSION_MULTIPLIER = -1.0 +_MINI_CHEETAH_SWING_CONVERSION_MULTIPLIER = 1.0 + + +def get_neutral_motor_angles(robot_class): + """Return a neutral (standing) pose for a given robot type. + + Args: + robot_class: This returns the class (not the instance) for the robot. + Currently it supports minitaur, laikago and mini-cheetah. + + Returns: + Pose object for the given robot. It's either MinitaurPose, LaikagoPose or + MiniCheetahPose. + + Raises: + ValueError: If the given robot_class is different than the supported robots. + """ + if str(robot_class) in [ + str(minitaur_v2.Minitaur) + ]: + init_pose = minitaur_pose_utils.leg_pose_to_motor_angles( + np.array( + attr.astuple( + minitaur_pose_utils.MinitaurPose( + swing_angle_0=_MINITAUR_DEFAULT_SWING_POS, + swing_angle_1=_MINITAUR_DEFAULT_SWING_POS, + swing_angle_2=_MINITAUR_DEFAULT_SWING_POS, + swing_angle_3=_MINITAUR_DEFAULT_SWING_POS, + extension_angle_0=_MINITAUR_DEFAULT_EXTENSION_POS, + extension_angle_1=_MINITAUR_DEFAULT_EXTENSION_POS, + extension_angle_2=_MINITAUR_DEFAULT_EXTENSION_POS, + extension_angle_3=_MINITAUR_DEFAULT_EXTENSION_POS)))) + elif str(robot_class) in [ + str(laikago.Laikago), + str(laikago_v2.Laikago), + ]: + init_pose = np.array( + attr.astuple( + laikago_pose_utils.LaikagoPose( + abduction_angle_0=0, + hip_angle_0=_LAIKAGO_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_0=_LAIKAGO_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_1=0, + hip_angle_1=_LAIKAGO_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_1=_LAIKAGO_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_2=0, + hip_angle_2=_LAIKAGO_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_2=_LAIKAGO_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_3=0, + hip_angle_3=_LAIKAGO_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_3=_LAIKAGO_NEUTRAL_POSE_KNEE_ANGLE))) + elif str(robot_class) == str(mini_cheetah.MiniCheetah): + init_pose = np.array( + attr.astuple( + mini_cheetah_pose_utils.MiniCheetahPose( + abduction_angle_0=0, + hip_angle_0=_MINI_CHEETAH_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_0=_MINI_CHEETAH_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_1=0, + hip_angle_1=_MINI_CHEETAH_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_1=_MINI_CHEETAH_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_2=0, + hip_angle_2=_MINI_CHEETAH_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_2=_MINI_CHEETAH_NEUTRAL_POSE_KNEE_ANGLE, + abduction_angle_3=0, + hip_angle_3=_MINI_CHEETAH_NEUTRAL_POSE_HIP_ANGLE, + knee_angle_3=_MINI_CHEETAH_NEUTRAL_POSE_KNEE_ANGLE))) + else: + init_pose = robot_class.get_neutral_motor_angles() + return init_pose + + +def convert_leg_pose_to_motor_angles(robot_class, leg_poses): + """Convert swing-extend coordinate space to motor angles for a robot type. + + Args: + robot_class: This returns the class (not the instance) for the robot. + Currently it supports minitaur, laikago and mini-cheetah. + leg_poses: A list of leg poses in [swing,extend] or [abduction, swing, + extend] space for all 4 legs. The order is [abd_0, swing_0, extend_0, + abd_1, swing_1, extend_1, ...] or [swing_0, extend_0, swing_1, extend_1, + ...]. Zero swing and zero extend gives a neutral standing pose for all the + robots. For minitaur, the conversion is fully accurate, for laikago and + mini-cheetah the conversion is approximate where swing is reflected to hip + and extend is reflected to both knee and the hip. + + Returns: + List of motor positions for the selected robot. The list include 8 or 12 + motor angles depending on the given robot type as an argument. Currently + laikago and mini-cheetah has motors for abduction which does not exist for + minitaur robot. + + Raises: + ValueError: Conversion fails due to wrong inputs. + """ + default_leg_order = ["front_left", "back_left", "front_right", "back_right"] + leg_order = default_leg_order + if len(leg_poses) not in [8, 12]: + raise ValueError("Dimension of the leg pose provided is not 8 or 12.") + neutral_motor_angles = get_neutral_motor_angles(robot_class) + motor_angles = leg_poses + # If it is a robot with 12 motors but the provided leg pose does not contain + # abduction, extend the pose to include abduction. + if len(neutral_motor_angles) == 12 and len(leg_poses) == 8: + for i in _ABDUCTION_ACTION_INDEXES: + motor_angles.insert(i, 0) + # If the robot does not have abduction (minitaur) but the input contains them, + # ignore the abduction angles for the conversion. + elif len(neutral_motor_angles) == 8 and len(leg_poses) == 12: + del leg_poses[::3] + # Minitaur specific conversion calculations using minitaur-specific safety + # limits. + if str(robot_class) in [ + + str(minitaur_v2.Minitaur) + ]: + motor_angles = minitaur_pose_utils.leg_pose_to_motor_angles_with_half_pi_offset_and_safety( + leg_poses) + # Laikago and mini-cheetah specific conversion calculations. + elif str(robot_class) in [ + str(mini_cheetah.MiniCheetah), + str(laikago.Laikago), + str(laikago_v2.Laikago), + + ]: + swing_scale = 1.0 + extension_scale = 1.0 + # Laikago specific conversion multipliers. + if str(robot_class) in [ + str(laikago.Laikago), + str(laikago_v2.Laikago), + + ]: + swing_scale = _LAIKAGO_SWING_CONVERSION_MULTIPLIER + extension_scale = _LAIKAGO_EXTENSION_CONVERSION_MULTIPLIER + leg_order = ["front_right", "front_left", "back_right", "back_left"] + # Mini-cheetah specific multipliers. + elif str(robot_class) in [str(mini_cheetah.MiniCheetah)]: + swing_scale = _MINI_CHEETAH_SWING_CONVERSION_MULTIPLIER + extension_scale = _MINI_CHEETAH_EXTENSION_CONVERSION_MULTIPLIER + # In this approximate conversion for mini-cheetah and laikago we set hip + # angle swing + half of the extend and knee angle to extend as rotation. + # We also scale swing and extend based on some hand-tuned constants. + multipliers = np.array([1.0, swing_scale, extension_scale] * 4) + swing_extend_scaled = leg_poses * multipliers + # Swing is (swing - half of the extension) due to the geometry of the leg. + extra_swing = swing_extend_scaled * ([0, 0, -0.5] * 4) + swing_extend_scaled += np.roll(extra_swing, -1) + motor_angles = list(swing_extend_scaled) + motor_angles = neutral_motor_angles + motor_angles + # Change the order of the legs if it is different for the specific robot. + if leg_order != default_leg_order: + leg_order = [default_leg_order.index(leg) for leg in leg_order] + ordered_motor_angles = [] + for i in leg_order: + ordered_motor_angles.extend(motor_angles[3 * i:3 * i + 3]) + motor_angles = ordered_motor_angles + else: + motor_angles = robot_class.convert_leg_pose_to_motor_angles(leg_poses) + + return motor_angles diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/termination_reason.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/termination_reason.py new file mode 100644 index 000000000..5a943d9e4 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/termination_reason.py @@ -0,0 +1,40 @@ +"""Enum for classifying the termination reason of an episode.""" + +import enum +import gin + + +@gin.constants_from_enum +class TerminationReason(enum.IntEnum): + """Enum that identifies termination reasons of an episode. + + For any new termination reason added here, please update the corresponding + termination reward files to make sure it is used properly. + """ + UNKNOWN = 0 + STEP_LIMIT = 1 + WALL_COLLISION = 2 + BAD_LOCATION = 3 + AGENT_COLLISION = 4 + GOAL_REACHED = 5 + INVALID_STEP_REVERT_AND_CONTINUE = 6 + INVALID_EPISODE = 7 + RUN_TIME_LIMIT = 8 + NOT_ADVANCING_LIMIT = 9 + NOT_LOCALIZED = 10 + + +COLORMAP = { + TerminationReason.UNKNOWN: (64, 64, 64), # Dark gray. + TerminationReason.STEP_LIMIT: (128, 64, 192), # Purple. + TerminationReason.WALL_COLLISION: (255, 64, 128), # Bright red. + TerminationReason.BAD_LOCATION: (255, 0, 192), # Magenta. + TerminationReason.AGENT_COLLISION: (255, 128, 0), # Orange. + TerminationReason.GOAL_REACHED: (96, 255, 96), # Bright green. + TerminationReason.INVALID_STEP_REVERT_AND_CONTINUE: (255, 255, + 255), # White. + TerminationReason.INVALID_EPISODE: (0, 0, 0), # Black. + TerminationReason.RUN_TIME_LIMIT: (128, 64, 192), # Purple. + TerminationReason.NOT_ADVANCING_LIMIT: (128, 64, 192), # Purple. + TerminationReason.NOT_LOCALIZED: (255, 0, 192), # Magenta. +} \ No newline at end of file diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/autonomous_object.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/autonomous_object.py new file mode 100644 index 000000000..44c9d65ec --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/autonomous_object.py @@ -0,0 +1,286 @@ +# Lint as: python3 +"""A module that defines autonomous object class and related functions.""" +from typing import Any, Callable, Dict, Optional, Sequence, Text, Union + +from absl import logging +import gin +import numpy as np + +from pybullet_utils import bullet_client +from pybullet_envs.minitaur.envs_v2 import base_client +from pybullet_envs.minitaur.envs_v2.sensors import sensor +from pybullet_envs.minitaur.robots import object_controller +from pybullet_envs.minitaur.robots import robot_base + +# The action value to pass into AutonomousObject pre_control_step() and +# apply_action(). +AUTONOMOUS_ACTION = None + +# Maximum force used in constraint based actuation. +_MAX_FORCE = 1000 + + +# TODO(b/155124699): find a better way actuate object than using constraint or +# modifying URDF. +@gin.configurable +class AutonomousObject(robot_base.RobotBase): + """Autonomous object that moves/acts in simulation guided by a controller.""" + + def __init__(self, + urdf_file: Text, + sensors: Sequence[sensor.Sensor] = (), + controller: object_controller.ControllerBase = None, + actuate_by_reset: bool = False): + """Constructor. + + Args: + urdf_file: The path to urdf file of the object. + sensors: A list of sensor objects to attach to autonomous object. + controller: A controller object that governs autonomous object's motion. + If not specified, StationaryController is used. + actuate_by_reset: Use pybullet resetBasePositionAndOrientation to actuate + the object. Default is False, which means actuate by constraint. In the + actuate by constrained mode, be extra cautious when the position or + orientation control is based on position or orientation sensor reading + of the same object. This loop-back condition is known to be problematic + and causes slower than expected motion. + """ + self._urdf_file = urdf_file + self._controller = controller or object_controller.StationaryController() + self._actuate_by_reset = actuate_by_reset + self._actuate_function = ( + self._actuate_base_pose + if not actuate_by_reset else self._reset_base_pose) + + self._sensors = list(sensors) + self._object_id = -1 + self._constraint_id = -1 + + self._pybullet_client = None # will be initialized in set_sim_client() + self._clock = None # will be initialized in set_clock() + self._init_internal_states() + + def _init_internal_states(self) -> None: + self._observations_time_since_reset = 0 + self._observations = {} + self._position = np.zeros(3) + self._orientation = np.array([0, 0, 0, 1]) + + def set_sim_client(self, pybullet_client: bullet_client.BulletClient) -> None: + """Sets new simulation client and reload assets.""" + self._pybullet_client = pybullet_client + self._init_internal_states() + self.load() + + def set_clock(self, clock: Callable[[], float]) -> None: + """Sets monotonic clock when adding into simulation environment.""" + self._clock = clock + + @property + def sim_object_id(self): + return self._object_id + + def update(self, time_since_reset_sec: float, + observations: Dict[Text, Any]) -> None: + """Updates simulation time and observations. + + This function should be called before apply_action in each simulation step. + + Args: + time_since_reset_sec: Time from start of simulation reset in seconds. + observations: A dict of observations. + """ + if time_since_reset_sec < self._observations_time_since_reset: + raise ValueError( + "Time cannot go backwards. Current t = %f, new t = %f." % + (self._observations_time_since_reset, time_since_reset_sec)) + self._observations_time_since_reset = time_since_reset_sec + self._observations = observations + + def _load_urdf(self): + """Loads object URDF file.""" + try: + print("loading: ", self._urdf_file) + self._object_id = self._pybullet_client.loadURDF(self._urdf_file) + except: + print("Error: cannot load ", self._urdf_file) + import sys + sys.exit(0) + + def load(self) -> None: + """Reconstructs the robot and resets its states.""" + self._load_urdf() + if not self._actuate_by_reset: + self._constraint_id = self._pybullet_client.createConstraint( + parentBodyUniqueId=self._object_id, + parentLinkIndex=-1, + childBodyUniqueId=-1, + childLinkIndex=-1, + jointType=self._pybullet_client.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, 0), + childFramePosition=(0, 0, 0), + childFrameOrientation=(0, 0, 0, 1)) + + for s in self._sensors: + s.set_robot(self) + + # Resets the pose and updates the initial observations. + self.reset() + + def reset( + self, + base_position: Optional[Sequence[float]] = None, + base_orientation_quaternion: Optional[Sequence[float]] = None, + controller: Optional[object_controller.ControllerBase] = None) -> None: + """Resets the states (e.g. + + pose and sensor readings) of the robot. + + This is called at the start of each episode by the environment. + + Args: + base_position: Robot base position after reset. Must be None. + base_orientation_quaternion: Robot base orientation after reset. Must be + None. + controller: A new controller to replace original controller. + """ + if base_position is not None or base_orientation_quaternion is not None: + raise ValueError("Reset position and orientation of AutonomousObject is " + "specified in controller.") + + if controller is not None: + self._controller = controller + + self._init_internal_states() + self._position, self._orientation, _ = self._controller.get_action( + object_controller.INIT_TIME, self._observations) + + self._reset_base_pose(self._position, self._orientation) + self.receive_observation() + + def terminate(self) -> None: + """Shuts down the robot, no-op in simulation.""" + + def pre_control_step(self, action: Any) -> Any: + """Processes the input action before the action repeat loop. + + Args: + action: expect it to be `AUTONOMOUS_ACTION` at present. + + Returns: + the action as is. + """ + # Environment should not pass action other than AUTONOMOUS_ACTION. + if action is not AUTONOMOUS_ACTION: + raise ValueError("AutonomousObject only accept AUTONOMOUS_ACTION as " + "action value input.") + return action + + def apply_action(self, action: Any) -> None: + """Applies the action to the robot.""" + # Environment should not pass action other than AUTONOMOUS_ACTION. + if action is not AUTONOMOUS_ACTION: + raise ValueError("AutonomousObject only accept AUTONOMOUS_ACTION as " + "action value input.") + position, orientation, _ = self._controller.get_action( + self._observations_time_since_reset, self._observations) + + self._actuate_function(position, orientation) + + def receive_observation(self) -> None: + """Updates the robot sensor readings.""" + position, orientation = ( + self._pybullet_client.getBasePositionAndOrientation(self._object_id)) + self._position = np.array(position) + self._orientation = np.array(orientation) + + def post_control_step(self) -> None: + """Updates internal variables. Not yet used in AutonomousObject.""" + pass + + def _reset_base_pose(self, + position: Union[Sequence[float], np.ndarray] = None, + orientation_quat: Union[Sequence[float], + np.ndarray] = None): + """Resets the base to the desired position and orientation. + + Args: + position: The desired base position. If omitted, current location is used. + orientation_quat: The desired base orientation in quaternion. If omitted, + current orientation is used. + """ + if position is None: + position = self._position + + if orientation_quat is None: + orientation_quat = self._orientation + + self._pybullet_client.resetBaseVelocity(self._object_id, (0, 0, 0), + (0, 0, 0)) + self._pybullet_client.resetBasePositionAndOrientation( + self._object_id, position, orientation_quat) + + def _actuate_base_pose(self, position: Union[Sequence[float], np.ndarray], + orientation_quat: Union[Sequence[float], np.ndarray]): + """Actuates the base to the desired position and orientation. + + Difference of this function from _reset_base_pose() is that this function + considers dynamics along the path and collisions along the motion path. + + Args: + position: The desired base position. + orientation_quat: The desired base orientation in quaternion. + """ + self._pybullet_client.changeConstraint( + self._constraint_id, + position, + jointChildFrameOrientation=orientation_quat, + maxForce=_MAX_FORCE) + + def _reset_joint_angles(self, joint_angles=None): + """Resets the joint angles. Not yet used in AutonomousObject.""" + del joint_angles + + @property + def action_names(self) -> Sequence[Text]: + """Returns a sequence of action names. Always () for AutonomousObject.""" + return () + + @property + def sensors(self) -> Sequence[sensor.Sensor]: + """Returns the sensors on this robot.""" + return self._sensors + + @property + def base_orientation_quaternion(self) -> np.ndarray: + """Returns the base pose as a quaternion in format (x, y, z, w).""" + return self._orientation.copy() + + @property + def base_roll_pitch_yaw(self) -> np.ndarray: + """Returns the base roll, pitch, and yaw angles in radians.""" + return np.array( + self._pybullet_client.getEulerFromQuaternion(self._orientation)) + + @property + def base_position(self) -> np.ndarray: + """Returns the base cartesian coordinates in meters.""" + return self._position.copy() + + @property + def timestamp(self): + """Simulation monotonic time.""" + if self._clock is None: + raise RuntimeError("Must call set_clock() before accessing timestamp.") + return self._clock() + + # This is need for CameraSensor.set_robot() to work. + @property + def pybullet_client(self): + return self._pybullet_client + + # This is need for CameraSensor.set_robot() to work. + @property + def robot_id(self) -> int: + return self._object_id diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/crowd_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/crowd_controller.py new file mode 100644 index 000000000..409c029cf --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/crowd_controller.py @@ -0,0 +1,706 @@ +# Lint as: python3 +"""Crowd objects/human controllers module.""" + +import abc +import collections +from typing import Any, Callable, Dict, Iterable, List, Optional, Union, Sequence, Text + +from absl import logging +import dataclasses +import gin +import numpy as np +#import rvo2 + +from pybullet_envs.minitaur.envs_v2.sensors import base_position_sensor +from pybullet_envs.minitaur.envs_v2.sensors import sensor as generic_sensor +from pybullet_envs.minitaur.robots import autonomous_object +from pybullet_envs.minitaur.robots import object_controller + + +POSITION_SENSOR_POSTFIX = "_pos" + + +@dataclasses.dataclass +class MovingObjectRecord: + position_key: Text + agent_id: int + radius: float + last_position: Optional[np.ndarray] = None + + +@gin.configurable +def sample_start_target_position(scene, + start=None, + start_circles=None, + target_circles=None, + num_sampling_retries=1, + min_wall_distance=0.0, + min_goal_euclidean_distance=0.0, + max_goal_euclidean_distance=np.Inf, + min_path_clearance=None): + """Sample valid start and target position reachable from start. + + Args: + scene: a SceneBase instance implementing get_random_valid_position function. + start: a 2-tuple (x, y) of start position. If specified, no start is + sampled. + start_circles: a list of circle specification. Each circle is specified as + a tuple ((x, y), r) of a center (x, y) and radius r. If specified, start + position is sampled from within one of the start_circles. + target_circles: same as start_circle. If specified, target positions is + sampled from within one of the start_circles. + num_sampling_retries: a positive int, number of attempts to sample a + start, target pair. + min_wall_distance: a float, the minimum distance to a wall. + min_goal_euclidean_distance: a positive float, the minimum distance between + start and target. + max_goal_euclidean_distance: a positive float, the maximum distance between + start and target. + min_path_clearance: float, clearance of shortest path to walls. + + Returns: + A 4 tuple (start, target, shortest_path, is_valid). start and target are + start and target positions, shortest_path is a list of 2-tuples specifying + the shortest path from start to target, is_valid is bool specifying whether + the start, target pair is valid. If min_path_clearance is not specified, + then shortest_path is None. + """ + if not hasattr(scene, "get_random_valid_position"): + raise ValueError( + "Incompatible scene {}. Expected to have `get_random_valid_position` " + "method.".format(scene)) + + def _print_counters(counters): + for name, value in counters.items(): + logging.info(" %s: %d", name, value) + + sampling_counters = collections.defaultdict(lambda: 0) + for _ in range(num_sampling_retries): + if start is None: + start_pos = scene.get_random_valid_position( + min_wall_distance, inclusion_circles=start_circles) + else: + if start_circles is not None: + raise ValueError("At most one of the arguments start and start_circles " + "can be not None.") + start_pos = start + target_pos = scene.get_random_valid_position( + min_wall_distance, inclusion_circles=target_circles) + sampling_counters["attempts"] += 1 + + euclidean_distance = np.linalg.norm(target_pos - start_pos) + if euclidean_distance < min_goal_euclidean_distance: + sampling_counters["min_euclidean"] += 1 + continue + if euclidean_distance > max_goal_euclidean_distance: + sampling_counters["max_euclidean"] += 1 + continue + + # Skip the path computation is no path clearance is provided. + if min_path_clearance is None: + logging.info("Valid goal with no minimum path clearance checking.") + _print_counters(sampling_counters) + return start_pos, target_pos, None, True + + # Check the goal clearance along the shortest path + if not hasattr(scene, "find_shortest_path"): + raise ValueError( + f"scene %s missing find_shortest_path method {scene}") + + # This is a slow process. + shortest_path = scene.find_shortest_path( + start_pos[:2], target_pos[:2], min_path_clearance) + # No path exists between current robot position and goal satisfying the + # clearance. + if shortest_path is None: + sampling_counters["path_clearance"] += 1 + continue + + logging.info("Valid start/target with path clearance checking.") + _print_counters(sampling_counters) + return start_pos, target_pos, shortest_path, True + + logging.info("No valid start/target found.") + _print_counters(sampling_counters) + return start_pos, target_pos, None, False + + +class CrowdController(metaclass=abc.ABCMeta): + """Crowd controller interface.""" + + def __init__(self, names: Iterable[Text], + position_key_formatter="%s" + POSITION_SENSOR_POSTFIX): + """Constructor. + + Args: + names: Name of instance (dynamic object or human). + position_key_formatter: Formatter to convert name to position sensor name. + """ + self._names = list(names) + self._position_key_formatter = position_key_formatter + self._num_instance = len(self._names) + + self._current_time = 0 + + def _validate_instance_id(self, instance_id): + if not 0 <= instance_id < self._num_instance: + raise ValueError( + f"instance_id must be an integer in [0, {self.num_instance}), " + f"got {instance_id}.") + + @property + def num_instance(self): + """Returns the number of crowd instances.""" + return self._num_instance + + def instance_name(self, instance_id: int) -> Text: + """Returns the name of instance.""" + self._validate_instance_id(instance_id) + return self._names[instance_id] + + def instance_controller( + self, instance_id: int) -> object_controller.ControllerBase: + """Returns the individual controller of certain instance.""" + self._validate_instance_id(instance_id) + return _IndividualController(self, instance_id) + + def instance_get_action( + self, instance_id: int, time_sec: float, + observations: Dict[Text, Any]) -> object_controller.ControllerOutput: + """Returns action of specific instance given observation. + + This method is for _IndividualController. + + Args: + instance_id: Identifier of an object in the crowd. + time_sec: Time since simulation reset in seconds. If time < 0, returns + initial values and ignores observations. + observations: A dict of all observations. + + Returns: + Position, orientation and an extra info dict for robot joints, human + skeletal pose, etc. + """ + if time_sec < 0: + self._recalculate_actions(object_controller.INIT_TIME, {}) + self._current_time = object_controller.INIT_TIME + elif time_sec > self._current_time: + self._current_time = time_sec + self._recalculate_actions(self._current_time, observations) + + self._validate_instance_id(instance_id) + + return self._get_action_of_instance(instance_id) + + @abc.abstractmethod + def _recalculate_actions( + self, time_sec: float, observations: Dict[Text, Any]) -> None: + """Calculates crowd command for all instances in crowd.""" + raise NotImplementedError( + "_recalculate_actions() should be implemented by subclass.") + + @abc.abstractmethod + def _get_action_of_instance( + self, instance_id: int) -> object_controller.ControllerOutput: + """Returns calculated actions of specific instance.""" + raise NotImplementedError( + "_get_action_of_instance() should be implemented by subclass.") + + def set_scene(self, scene) -> None: + """Sets the scene for crowd controller to obtain scene information.""" + del scene + + +class _IndividualController(object_controller.ControllerBase): + """A utility class that wraps crowd controller in ControllerBase interface.""" + + def __init__(self, crowd_controller: CrowdController, instance_id: int): + """Constructor. + + Args: + crowd_controller: The controller of crowd to which this instance belong. + instance_id: Identifier of a crowd instance. + """ + self._instance_id = instance_id + self._crowd_controller = crowd_controller + + def get_action( + self, time_sec: float, + observations: Dict[Text, Any]) -> object_controller.ControllerOutput: + """Returns position, orientation and pose based on time and observations. + + Args: + time_sec: Time since simulation reset in seconds. If time < 0, returns + initial values and ignores observations. + observations: A dict of all observations. + + Returns: + Position, orientation and an extra info dict for robot joints, human + skeletal pose, etc. + """ + return self._crowd_controller.instance_get_action( + self._instance_id, time_sec, observations) + + +@gin.configurable +class StationaryController(CrowdController): + """A crowd controller that places crowd objects at fixed positions.""" + + def __init__( + self, positions: Sequence[Sequence[float]], + orientations: Optional[Sequence[Sequence[float]]] = None, **kwargs): + """Constructor. + + Args: + positions: Fixed positions (3D points) of crowd instances. + orientations: Fixed orientations in quaternion of crowd instances. + **kwargs: Keyword arguments to pass on to base class. + """ + super().__init__(**kwargs) + + if orientations is None: + orientations = np.array(((0, 0, 0, 1),) * self.num_instance) + + if not len(positions) == len(orientations) == self.num_instance: + raise ValueError( + f"positions and orientations should all have the same length " + f"{self.num_instance}. Got len(positions) = {len(positions)}, " + f"len(orientations) = {len(orientations)}.") + + self._positions = positions + self._orientations = orientations + + def _recalculate_actions( + self, time_sec: float, observations: Dict[Text, Any]) -> None: + """Calculates crowd command for all instances in crowd.""" + del time_sec + del observations + + def _get_action_of_instance( + self, instance_id: int) -> object_controller.ControllerOutput: + """Returns calculated actions of specific instance.""" + self._validate_instance_id(instance_id) + return self._positions[instance_id], self._orientations[instance_id], {} + + +@gin.configurable +class OrcaController(CrowdController): + """A crowd controller that controls crowd instances using ORCA algorithm. + + Crowd instance will be initialized at a specified start position and move + towards specified target position in a linear path while avoid collision with + each other. + """ + + _DEFAULT_NEIGHBOR_DISTANCE_M = 5 + _DEFAULT_MAX_NEIGHBORS = 10 + _DEFAULT_RADIUS_M = 0.5 + _DEFAULT_MAX_SPEED_MPS = 2 + _DEFAULT_TIME_HORIZON_SEC = 1.0 + _DEFAULT_OBSTACLE_TIME_HORIZON_SEC = 0.3 + + def __init__( + self, + timestep: float, + start_positions: Optional[Sequence[Sequence[float]]] = None, + target_positions: Optional[Sequence[Sequence[float]]] = None, + use_position_generator: Optional[bool] = False, + group_sizes: Sequence[int] = None, + radius: float = _DEFAULT_RADIUS_M, + max_speed_mps: float = _DEFAULT_MAX_SPEED_MPS, + time_horizon_sec: float = _DEFAULT_TIME_HORIZON_SEC, + obstacle_time_horizon_sec: float = _DEFAULT_OBSTACLE_TIME_HORIZON_SEC, + neighbor_distance_m: float = _DEFAULT_NEIGHBOR_DISTANCE_M, + max_neighbors: int = _DEFAULT_MAX_NEIGHBORS, + workaround_erp_issue: bool = True, + moving_objects_pos_key: Sequence[Text] = (), + moving_objects_radius: Union[float, Sequence[float]] = _DEFAULT_RADIUS_M, + endless_trajectory: bool = True, + **kwargs): + """Constructor. + + Args: + timestep: Timestep of simulation. + start_positions: A list of position (x, y, z) for crowd instances as + their starting position. + target_positions: A list of position (x, y, z) for crowd instances as + their target position. + use_position_generator: a boolean, if True than the start and end + positions are sampled. start_positions and target_positions must be None + group_sizes: If set, then crowd is split in groups randomly, whose sizes + are picked in random from this group_size list. In this way, the + crowd simulator sumulaters clusters of objects moving around. + radius: Radius of crowd instances. + max_speed_mps: Maximum crowd instance speed. + time_horizon_sec: Time horizon in second. + obstacle_time_horizon_sec: Time horizon for static obstacle in second. + neighbor_distance_m: Neighbor distance in meters. Instances closer than + this distance are considered neighbors. + max_neighbors: Max number of neighbors. + workaround_erp_issue: There is an issue with pybullet constraint that the + constraint is solved only 20% per timestep. Need to amplify position + delta by 5x to workaround this issue. + moving_objects_pos_key: Position observation key of moving objects not + controlled by the ORCA controller. + moving_objects_radius: Radius of moving objects. Should be a float, which + applies to all moving objects, or a sequence of float, which should be + of the same length as moving_objects_pos_key. + endless_trajectory: Only valid if use_position_generator is True. Agent + returns to starting point after reaching goal to achieve endless motion. + **kwargs: Keyword arguments to pass on to base class. + """ + super().__init__(**kwargs) + + assert ((start_positions is not None and target_positions is not None) or + use_position_generator) + if not use_position_generator: + if not len(start_positions) == len(target_positions) == self.num_instance: + raise ValueError( + f"start_positions and target_positions should both have length " + f"equals {self.num_instance}: " + f"len(start_positions) = {len(start_positions)}, " + f"len(target_positions) = {len(target_positions)}.") + + self._timestep = timestep + self._radius = radius + self._max_speed_mps = max_speed_mps + self._time_horizon_sec = time_horizon_sec + self._obstacle_time_horizon_sec = obstacle_time_horizon_sec + self._neighbor_distance_m = neighbor_distance_m + self._max_neighbors = max_neighbors + self._use_position_generator = use_position_generator + self._endless_trajectory = endless_trajectory + self._scene = None + if isinstance(moving_objects_radius, float): + moving_objects_radius = [ + moving_objects_radius] * len(moving_objects_pos_key) + if len(moving_objects_radius) != len(moving_objects_pos_key): + raise ValueError( + "moving_objects_radius should be either a float or a sequence of " + "float with the same length as moving_objects_pos_key.") + self._moving_objects = [ + MovingObjectRecord(position_key=key, agent_id=-1, radius=radius) + for key, radius in zip(moving_objects_pos_key, moving_objects_radius)] + + self._paths = None + self._path_indices = None + if self._use_position_generator: + self._start_positions = None + self._target_positions = None + else: + self._start_positions = np.array(start_positions, dtype=np.float64) + self._target_positions = np.array(target_positions, dtype=np.float64) + # A guard against multiple initializations. See recalculate_actions below. + self._already_initialized = False + self._group_sizes = [1] if group_sizes is None else group_sizes + + # The following variables are initialized in _recalculate_actions() + self._current_positions = None + self._command_positions = None + self._command_orientations = None + + #self._orca = rvo2.PyRVOSimulator( + # self._timestep, # timestep + # self._neighbor_distance_m, # neighborDist + # self._max_neighbors, # maxNeighbors + # self._time_horizon_sec, # timeHorizon + # self._obstacle_time_horizon_sec, # timeHorizonObst + # self._radius, # radius + # self._max_speed_mps # maxSpeed + #) + for i in range(self.num_instance): + if self._use_position_generator: + start_position = (0, 0) + else: + start_position = self._start_positions[i, :2] + agent_id = self._orca.addAgent( + tuple(start_position), + self._neighbor_distance_m, # neighborDist + self._max_neighbors, # maxNeighbors + self._time_horizon_sec, # timeHorizon + self._obstacle_time_horizon_sec, # timeHorizonObst + self._radius, # radius + self._max_speed_mps, # maxSpeed + (0.0, 0.0)) # velocity + assert agent_id == i + + for obj in self._moving_objects: + obj.agent_id = self._orca.addAgent( + (0.0, 0.0), # position (will adjust after simulation starts) + self._neighbor_distance_m, # neighborDist + self._max_neighbors, # maxNeighbors + self._timestep, # timeHorizon + self._timestep, # timeHorizonObst + obj.radius, # radius + self._max_speed_mps, # maxSpeed + (0.0, 0.0)) # velocity + + self._workaround_erp_issue = workaround_erp_issue + + def _subsample_path(self, path, subsample_step=1.0): + subsampled_path = [path[0]] + traveled_dist = 0.0 + for i, (s, t) in enumerate(zip(path[:-1], path[1:])): + traveled_dist += np.sqrt( + np.square(s[0] - t[0]) + np.square(s[1] - t[1])) + if traveled_dist > subsample_step or i >= len(path) - 2: + subsampled_path.append(t) + traveled_dist = 0.0 + return subsampled_path + + def _generate_start_target_positions(self): + """Generates start and target positions using goal generartors.""" + assert self._scene is not None + self._start_positions = np.zeros((self.num_instance, 3), dtype=np.float64) + self._target_positions = np.zeros((self.num_instance, 3), dtype=np.float64) + + self._paths = [] + self._path_indices = [] + start_circles, target_circles = None, None + group_radius = 1.0 + current_group_size = np.random.choice(self._group_sizes) + index_in_current_group = 0 + for i in range(self._num_instance): + start_pos, target_pos, path, is_valid = sample_start_target_position( + self._scene, + start_circles=start_circles, + target_circles=target_circles) + if index_in_current_group == current_group_size - 1: + start_circles, target_circles = None, None + index_in_current_group = 0 + current_group_size = np.random.choice(self._group_sizes) + else: + if start_circles is None: + start_circles = [(start_pos[:2], group_radius)] + target_circles = [(target_pos[:2], group_radius)] + else: + start_circles += [(start_pos[:2], group_radius)] + target_circles += [(target_pos[:2], group_radius)] + index_in_current_group += 1 + if not is_valid: + raise ValueError("No valid start/target positions.") + self._start_positions[i, :] = start_pos + self._target_positions[i, :] = target_pos + + subsampled_path = self._subsample_path(path) + self._paths.append(np.array(subsampled_path, dtype=np.float32)) + self._path_indices.append(0) + + def _recalculate_actions( + self, time_sec: float, observations: Dict[Text, Any]) -> None: + """Calculates crowd command for all crowd instances.""" + if self._use_position_generator: + if (time_sec == object_controller.INIT_TIME and + self._start_positions is None and + not self._already_initialized): + self._generate_start_target_positions() + # Initialize only once per initial time even if recalculate actions + # is called multiple times. + self._already_initialized = True + if time_sec == object_controller.INIT_TIME: + # Resets orca simulator. + for i in range(len(self._names)): + self._orca.setAgentPosition(i, tuple(self._start_positions[i, :2])) + + self._command_positions = self._start_positions.copy() + self._current_positions = self._start_positions.copy() + self._command_orientations = np.repeat( + ((0.0, 0.0, 0.0, 1.0),), len(self._names), axis=0) + self._last_target_recalculation_sec = time_sec + return + else: + # The moment we step beyond initial time, we can initialize again. + self._already_initialized = False + + if self._use_position_generator: + for i in range(self._num_instance): + dist = np.linalg.norm( + self._current_positions[i, :] - self._target_positions[i, :]) + if dist < 2.0: + _, target_pos, path, is_valid = sample_start_target_position( + self._scene, self._current_positions[i, :]) + if is_valid: + self._target_positions[i, :] = target_pos + subsampled_path = self._subsample_path(path) + self._paths.append(np.array(subsampled_path, dtype=np.float32)) + self._path_indices.append(0) + + # Sets agent position and preferred velocity based on target. + for i, agent_name in enumerate(self._names): + position = observations[self._position_key_formatter % agent_name] + self._orca.setAgentPosition( + i, tuple(position[:2])) # ORCA uses 2D position. + self._current_positions[i, :2] = position[:2] + + if self._paths is not None: + # Find closest point on the path from start to target, which (1) hasn't + # been covered already; (2) is at least max_coverage_distance away from + # current position. + distances = np.sqrt(np.sum(np.square( + self._paths[i] - position[:2]), axis=1)) + max_coverage_distance = 1.0 + index = self._path_indices[i] + while True: + if index >= len(self._paths[i]) - 1: + if self._endless_trajectory: + self._paths[i] = self._paths[i][::-1] + distances = distances[::-1] + index = 0 + break + elif distances[index] > max_coverage_distance: + break + else: + index += 1 + self._path_indices[i] = index + target_position = self._paths[i][index, :] + else: + target_position = self._target_positions[i][:2] + + goal_vector = target_position - position[:2] + goal_vector_norm = np.linalg.norm(goal_vector) + np.finfo(np.float32).eps + goal_unit_vector = goal_vector / goal_vector_norm + + kv = 1 + velocity = min(kv * goal_vector_norm, + self._DEFAULT_MAX_SPEED_MPS) * goal_unit_vector + self._orca.setAgentPrefVelocity(i, tuple(velocity)) + + for obj in self._moving_objects: + position = observations[obj.position_key] + self._orca.setAgentPosition(obj.agent_id, tuple(position[:2])) + if obj.last_position is None: + self._orca.setAgentPrefVelocity(obj.agent_id, (0.0, 0.0)) + else: + velocity = (position - obj.last_position) / self._timestep + self._orca.setAgentPrefVelocity(obj.agent_id, tuple(velocity[:2])) + obj.last_position = position.copy() + + # Advances orca simulator. + self._orca.doStep() + + # Retrieve agent position and save in buffer. + for i in range(len(self._names)): + x, y = self._orca.getAgentPosition(i) + self._command_positions[i, :2] = (x, y) + + yaw = np.arctan2(y - self._current_positions[i, 1], + x - self._current_positions[i, 0]) + self._command_orientations[i] = (0, 0, np.sin(yaw / 2), np.cos(yaw / 2)) + + def _get_action_of_instance( + self, instance_id) -> object_controller.ControllerOutput: + """Returns calculated actions of specific instance.""" + + if self._command_positions is None: + raise RuntimeError( + "Attempted to get action of instance before _recalculate_actions().") + + self._validate_instance_id(instance_id) + + if self._workaround_erp_issue: + k_erp = 1 / 0.2 + delta_position = ( + self._command_positions[instance_id] - + self._current_positions[instance_id]) + command_position = ( + self._current_positions[instance_id] + k_erp * delta_position) + else: + command_position = self._command_positions[instance_id].copy() + return command_position, self._command_orientations[instance_id], {} + + def set_scene(self, scene) -> None: + """Sets the scene for crowd controller to obtain scene information.""" + try: + polygons = scene.vectorized_map + for polygon in polygons: + self._orca.addObstacle([tuple(point) for point in polygon]) + self._orca.processObstacles() + self._scene = scene + except NotImplementedError: + logging.exception("Scene does not implement vectorized_map property. " + "Crowd agent cannot avoid static obstacles.") + + +@gin.configurable +def uniform_object_factory( + instance_id: int, + object_factory: Callable[..., autonomous_object.AutonomousObject], + *args, **kwargs) -> autonomous_object.AutonomousObject: + """A wrapper that removes instance_id in default crowd object factory.""" + del instance_id + return object_factory(*args, **kwargs) + + +@gin.configurable +def random_object_factory( + instance_id: int, + object_factories: Iterable[ + Callable[..., autonomous_object.AutonomousObject]], + *args, **kwargs) -> autonomous_object.AutonomousObject: + """A wrapper that removes instance_id in default crowd object factory.""" + del instance_id + object_factory = np.random.choice(object_factories) + return object_factory(*args, **kwargs) + + +@gin.configurable +def sensor_factory(instance_id: int, sensor: Callable[..., + generic_sensor.Sensor], + *args, **kwargs) -> generic_sensor.Sensor: + del instance_id + return sensor(*args, **kwargs) + + +@gin.configurable +class CrowdBuilder(object): + """A helper class to construct a crowd.""" + + def __init__( + self, + num_instance: int, + crowd_controller_factory: Callable[..., CrowdController], + object_factory: Callable[..., autonomous_object.AutonomousObject], + sensor_factories: Iterable[Callable[..., generic_sensor.Sensor]] = None): + """Constructor. + + Args: + num_instance: Number of autonomous objects in the crowd. + crowd_controller_factory: A callable that returns a crowd controller + object. + object_factory: Callable that returns an autonomous object. + sensor_factories: list of sensor callables. + """ + self._objects = [] + crowd_id_prefix = "crowd" + names = [crowd_id_prefix + "_%d" % i for i in range(num_instance)] + + self._controller = crowd_controller_factory(names=names) + + for i in range(num_instance): + position_sensor = base_position_sensor.BasePositionSensor( + name=names[i] + POSITION_SENSOR_POSTFIX) + + # Add additional per agent sensors (e.g. camera, occupancy, etc.). + add_sensors = [] + if sensor_factories: + for s in sensor_factories: + add_sensors.append( + sensor_factory( + instance_id=i, sensor=s, name=names[i] + "_" + s.__name__)) + + an_object = object_factory( + instance_id=i, + sensors=(position_sensor,) + tuple(add_sensors), + controller=self._controller.instance_controller(i)) + + self._objects.append(an_object) + + @property + def crowd_objects(self) -> List[autonomous_object.AutonomousObject]: + """Returns list of AutonomousObjects in the crowd.""" + return self._objects + + @property + def crowd_controller(self) -> CrowdController: + """Returns the crowd controller.""" + return self._controller diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/hybrid_motor_model.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/hybrid_motor_model.py new file mode 100644 index 000000000..4a15422b9 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/hybrid_motor_model.py @@ -0,0 +1,278 @@ +# Lint as: python3 +"""A generic PD motor model.""" + +from typing import Tuple, Union +import gin +import numpy as np + +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots import time_ordered_buffer + +_DEFAULT_BUFFER_SIZE = 200 + +_HYBRID_ACTION_LEN = len(robot_config.HybridActionIndex) +_HYBRID_POS_INDEX = robot_config.HybridActionIndex.POSITION.value +_HYBRID_KP_INDEX = robot_config.HybridActionIndex.POSITION_GAIN.value +_HYBRID_VEL_INDEX = robot_config.HybridActionIndex.VELOCITY.value +_HYBRID_KD_INDEX = robot_config.HybridActionIndex.VELOCITY_GAIN.value +_HYBRID_TORQUE_INDEX = robot_config.HybridActionIndex.TORQUE.value + + +def _convert_to_np_array(inputs: Union[float, Tuple[float], np.ndarray], dim): + """Converts the inputs to a numpy array. + + Args: + inputs: The input scalar or array. + dim: The dimension of the converted numpy array. + + Returns: + The converted numpy array. + + Raises: + ValueError: If the inputs is an array whose dimension does not match the + provied dimension. + """ + outputs = None + if isinstance(inputs, (tuple, np.ndarray)): + outputs = np.array(inputs) + else: + outputs = np.full(dim, inputs) + + if len(outputs) != dim: + raise ValueError("The inputs array has a different dimension {}" + " than provided, which is {}.".format(len(outputs), dim)) + + return outputs + + +@gin.configurable +class HybridMotorModel(object): + """A simple motor model that supports proportional and derivative control. + + When in POSITION mode, the torque is calculated according to the difference + between current and desired joint angle, as well as the joint velocity + differences. For more information about PD control, please refer to: + https://en.wikipedia.org/wiki/PID_controller. + + The model supports a HYBRID mode in which each motor command can be a tuple + (desired_motor_angle, position_gain, desired_motor_velocity, velocity_gain, + torque). + """ + + def __init__( + self, + num_motors: int, + pd_latency: float = 0, + motor_control_mode=robot_config.MotorControlMode.POSITION, + kp: Union[float, Tuple[float], np.ndarray] = 60, + kd: Union[float, Tuple[float], np.ndarray] = 1, + strength_ratios: Union[float, Tuple[float], np.ndarray] = 1, + torque_lower_limits: Union[float, Tuple[float], np.ndarray] = None, + torque_upper_limits: Union[float, Tuple[float], np.ndarray] = None, + ): + """Initializes the class. + + Args: + num_motors: The number of motors for parallel computation. + pd_latency: Simulates the motor controller's latency in reading motor + angles and velocities. + motor_control_mode: Can be POSITION, TORQUE, or HYBRID. In POSITION + control mode, the PD formula is used to track a desired position and a + zero desired velocity. In TORQUE control mode, we assume a pass through + of the provided torques. In HYBRID control mode, the users need to + provie (desired_position, position_gain, desired_velocity, + velocity_gain, feedfoward_torque) for each motor. + kp: The default position gains for motors. + kd: The default velocity gains for motors. + strength_ratios: The scaling ratio for motor torque outputs. This can be + useful for quick debugging when sim-to-real gap is observed in the + actuator behavior. + torque_lower_limits: The lower bounds for torque outputs. + torque_upper_limits: The upper bounds for torque outputs. The output + torques will be clipped by the lower and upper bounds. + + Raises: + ValueError: If the number of motors provided is negative or zero. + """ + if num_motors <= 0: + raise ValueError( + "Number of motors must be positive, not {}".format(num_motors)) + self._num_motors = num_motors + self._zero_array = np.full(num_motors, 0) + self._pd_latency = pd_latency + self._hybrid_command_dim = _HYBRID_ACTION_LEN * self._num_motors + self.set_motor_gains(kp, kd) + self.set_strength_ratios(strength_ratios) + self._torque_lower_limits = None + if torque_lower_limits: + self._torque_lower_limits = _convert_to_np_array(torque_lower_limits, + self._num_motors) + + self._torque_upper_limits = None + if torque_upper_limits: + self._torque_upper_limits = _convert_to_np_array(torque_upper_limits, + self._num_motors) + self._motor_control_mode = motor_control_mode + + # The history buffer is used to simulate the pd latency effect. + # TODO(b/157786642): remove hacks on duplicate timestep once the sim clock + # is fixed. + self._observation_buffer = time_ordered_buffer.TimeOrderedBuffer( + max_buffer_timespan=pd_latency, + error_on_duplicate_timestamp=False, + replace_value_on_duplicate_timestamp=True) + + def set_strength_ratios( + self, + strength_ratios: Union[float, Tuple[float], np.ndarray], + ): + """Sets the strength of each motor relative to the default value. + + Args: + strength_ratios: The relative strength of motor output, ranging from [0, + 1] inclusive. + """ + self._strength_ratios = np.clip( + _convert_to_np_array(strength_ratios, self._num_motors), 0, 1) + + def set_motor_gains( + self, + kp: Union[float, Tuple[float], np.ndarray], + kd: Union[float, Tuple[float], np.ndarray], + ): + """Sets the gains of all motors. + + These gains are PD gains for motor positional control. kp is the + proportional gain and kd is the derivative gain. + + Args: + kp: Proportional gain of the motors. + kd: Derivative gain of the motors. + """ + self._kp = _convert_to_np_array(kp, self._num_motors) + self._kd = _convert_to_np_array(kd, self._num_motors) + + def get_motor_gains(self): + """Get the PD gains of all motors. + + Returns: + Proportional and derivative gain of the motors. + """ + return self._kp, self._kd + + def reset(self): + self._observation_buffer.reset() + + def update(self, timestamp, true_motor_positions: np.ndarray, + true_motor_velocities: np.ndarray): + # Push these to the buffer + self._observation_buffer.add(timestamp, + (true_motor_positions, true_motor_velocities)) + + def get_motor_torques( + self, + motor_commands: np.ndarray, + motor_control_mode=None) -> Tuple[np.ndarray, np.ndarray]: + """Computes the motor torques. + + Args: + motor_commands: The desired motor angle if the motor is in position + control mode. The pwm signal if the motor is in torque control mode. + motor_control_mode: A MotorControlMode enum. + + Returns: + observed_torque: The torque observed. This emulates the limitations in + torque measurement, which is generally obtained from current estimations. + actual_torque: The torque that needs to be applied to the motor. + + Raises: + NotImplementedError if the motor_control_mode is not supported. + + """ + if not motor_control_mode: + motor_control_mode = self._motor_control_mode + + motor_torques = None + + if motor_control_mode is robot_config.MotorControlMode.TORQUE: + motor_torques = motor_commands + + if motor_control_mode is robot_config.MotorControlMode.POSITION: + motor_torques = self._compute_pd_torques( + desired_motor_angles=motor_commands, + kp=self._kp, + desired_motor_velocities=self._zero_array, + kd=self._kd) + + if motor_control_mode is robot_config.MotorControlMode.HYBRID: + motor_torques = self._compute_hybrid_action_torques(motor_commands) + + if motor_torques is None: + raise ValueError( + "{} is not a supported motor control mode".format(motor_control_mode)) + + # Rescale and clip the motor torques as needed. + motor_torques = self._strength_ratios * motor_torques + if (self._torque_lower_limits is not None or + self._torque_upper_limits is not None): + motor_torques = np.clip(motor_torques, self._torque_lower_limits, + self._torque_upper_limits) + + return motor_torques, motor_torques + + def get_motor_states(self, latency=None): + """Computes observation of motor angle and velocity under latency.""" + if latency is None: + latency = self._pd_latency + buffer = self._observation_buffer.get_delayed_value(latency) + angle_vel_t0 = buffer.value_0 + angle_vel_t1 = buffer.value_1 + coeff = buffer.coeff + + pos_idx = 0 + motor_angles = angle_vel_t0[pos_idx] * ( + 1 - coeff) + coeff * angle_vel_t1[pos_idx] + vel_idx = 1 + motor_velocities = angle_vel_t0[vel_idx] * ( + 1 - coeff) + coeff * angle_vel_t1[vel_idx] + return motor_angles, motor_velocities + + def _compute_pd_torques( + self, + desired_motor_angles: np.ndarray, + kp: np.ndarray, + desired_motor_velocities, + kd: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """Computes the pd torques. + + Args: + desired_motor_angles: The motor angles to track. + kp: The position gains. + desired_motor_velocities: The motor velocities to track. + kd: The velocity gains. + + Returns: + The computed motor torques. + """ + motor_angles, motor_velocities = self.get_motor_states() + motor_torques = -kp * (motor_angles - desired_motor_angles) - kd * ( + motor_velocities - desired_motor_velocities) + + return motor_torques + + def _compute_hybrid_action_torques( + self, motor_commands: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Computes the pd torques in the HYBRID mode.""" + assert len(motor_commands) == self._hybrid_command_dim + kp = motor_commands[_HYBRID_KP_INDEX::_HYBRID_ACTION_LEN] + kd = motor_commands[_HYBRID_KD_INDEX::_HYBRID_ACTION_LEN] + desired_motor_angles = motor_commands[_HYBRID_POS_INDEX::_HYBRID_ACTION_LEN] + desired_motor_velocities = motor_commands[ + _HYBRID_VEL_INDEX::_HYBRID_ACTION_LEN] + additional_torques = motor_commands[ + _HYBRID_TORQUE_INDEX::_HYBRID_ACTION_LEN] + + return self._compute_pd_torques(desired_motor_angles, kp, + desired_motor_velocities, + kd) + additional_torques diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago.py new file mode 100644 index 000000000..bcfcbdc3e --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago.py @@ -0,0 +1,319 @@ +"""Pybullet simulation of a Laikago robot.""" +import math +import os +import re +import gin +import numpy as np +from pybullet_utils import transformations +from pybullet_envs.minitaur.envs_v2.utilities import laikago_pose_utils +from pybullet_envs.minitaur.robots import laikago_constants +from pybullet_envs.minitaur.robots import laikago_motor +from pybullet_envs.minitaur.robots import minitaur +from pybullet_envs.minitaur.robots import robot_config + +NUM_MOTORS = 12 +NUM_LEGS = 4 +MOTOR_NAMES = [ + "FR_hip_motor_2_chassis_joint", + "FR_upper_leg_2_hip_motor_joint", + "FR_lower_leg_2_upper_leg_joint", + "FL_hip_motor_2_chassis_joint", + "FL_upper_leg_2_hip_motor_joint", + "FL_lower_leg_2_upper_leg_joint", + "RR_hip_motor_2_chassis_joint", + "RR_upper_leg_2_hip_motor_joint", + "RR_lower_leg_2_upper_leg_joint", + "RL_hip_motor_2_chassis_joint", + "RL_upper_leg_2_hip_motor_joint", + "RL_lower_leg_2_upper_leg_joint", +] +INIT_RACK_POSITION = [0, 0, 1] +INIT_POSITION = [0, 0, 0.48] +JOINT_DIRECTIONS = np.array([-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]) +HIP_JOINT_OFFSET = 0.0 +UPPER_LEG_JOINT_OFFSET = -0.6 +KNEE_JOINT_OFFSET = 0.66 +DOFS_PER_LEG = 3 +JOINT_OFFSETS = np.array( + [HIP_JOINT_OFFSET, UPPER_LEG_JOINT_OFFSET, KNEE_JOINT_OFFSET] * 4) +PI = math.pi + +MAX_MOTOR_ANGLE_CHANGE_PER_STEP = 0.12 +_DEFAULT_HIP_POSITIONS = ( + (0.21, -0.1157, 0), + (0.21, 0.1157, 0), + (-0.21, -0.1157, 0), + (-0.21, 0.1157, 0), +) + +# Bases on the readings from Laikago's default pose. +INIT_MOTOR_ANGLES = np.array([ + laikago_pose_utils.LAIKAGO_DEFAULT_ABDUCTION_ANGLE, + laikago_pose_utils.LAIKAGO_DEFAULT_HIP_ANGLE, + laikago_pose_utils.LAIKAGO_DEFAULT_KNEE_ANGLE +] * NUM_LEGS) + +CHASSIS_NAME_PATTERN = re.compile(r"\w+_chassis_\w+") +MOTOR_NAME_PATTERN = re.compile(r"\w+_hip_motor_\w+") +KNEE_NAME_PATTERN = re.compile(r"\w+_lower_leg_\w+") +TOE_NAME_PATTERN = re.compile(r"jtoe\d*") + +URDF_NO_TOES = "laikago.urdf" +URDF_WITH_TOES = "laikago_toes_zup.urdf" + +_BODY_B_FIELD_NUMBER = 2 +_LINK_A_FIELD_NUMBER = 3 + + +@gin.configurable +class Laikago(minitaur.Minitaur): + """A simulation for the Laikago robot.""" + + def __init__(self, urdf_filename=URDF_WITH_TOES, **kwargs): + self._urdf_filename = urdf_filename + if "motor_kp" not in kwargs: + kwargs["motor_kp"] = 100.0 + if "motor_kd" not in kwargs: + kwargs["motor_kd"] = 2.0 + if "motor_torque_limits" not in kwargs: + kwargs["motor_torque_limits"] = None + + # enable_clip_motor_commands: Boolean indicating if clipping should be + # applied to motor commands, which limits the amount of change in joint + # pose between timesteps. + if "enable_clip_motor_commands" in kwargs: + self._enable_clip_motor_commands = kwargs["enable_clip_motor_commands"] + del kwargs["enable_clip_motor_commands"] + else: + self._enable_clip_motor_commands = False + + # The follwing parameters are fixed for the Laikago robot. + kwargs["num_motors"] = NUM_MOTORS + kwargs["dofs_per_leg"] = DOFS_PER_LEG + kwargs["motor_direction"] = JOINT_DIRECTIONS + kwargs["motor_offset"] = JOINT_OFFSETS + kwargs["motor_overheat_protection"] = False + kwargs["motor_model_class"] = laikago_motor.LaikagoMotorModel + kwargs["safety_config"] = None + + super(Laikago, self).__init__(**kwargs) + + def _LoadRobotURDF(self): + laikago_urdf_path = self.GetURDFFile() + if self._self_collision_enabled: + self.quadruped = self._pybullet_client.loadURDF( + laikago_urdf_path, + self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation(), + flags=self._pybullet_client.URDF_USE_SELF_COLLISION) + else: + self.quadruped = self._pybullet_client.loadURDF( + laikago_urdf_path, self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation()) + + def _SettleDownForReset(self, default_motor_angles, reset_time): + self.ReceiveObservation() + + if reset_time <= 0: + return + + for _ in range(500): + self._StepInternal( + INIT_MOTOR_ANGLES, + motor_control_mode=robot_config.MotorControlMode.POSITION) + if default_motor_angles is not None: + num_steps_to_reset = int(reset_time / self.time_step) + for _ in range(num_steps_to_reset): + self._StepInternal( + default_motor_angles, + motor_control_mode=robot_config.MotorControlMode.POSITION) + + def GetHipPositionsInBaseFrame(self): + return _DEFAULT_HIP_POSITIONS + + def GetFootContacts(self): + all_contacts = self._pybullet_client.getContactPoints(bodyA=self.quadruped) + + contacts = [False, False, False, False] + for contact in all_contacts: + # Ignore self contacts + if contact[_BODY_B_FIELD_NUMBER] == self.quadruped: + continue + try: + toe_link_index = self._foot_link_ids.index( + contact[_LINK_A_FIELD_NUMBER]) + contacts[toe_link_index] = True + except ValueError: + continue + return contacts + + def ComputeJacobian(self, leg_id): + """Compute the Jacobian for a given leg.""" + # Because of the default rotation in the Laikago URDF, we need to reorder + # the rows in the Jacobian matrix. + if self._urdf_filename == URDF_WITH_TOES: + return super(Laikago, self).ComputeJacobian(leg_id) + else: + return super(Laikago, self).ComputeJacobian(leg_id)[(2, 0, 1), :] + + def ResetPose(self, add_constraint): + del add_constraint + for name in self._joint_name_to_id: + joint_id = self._joint_name_to_id[name] + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(joint_id), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=0) + for name, i in zip(MOTOR_NAMES, range(len(MOTOR_NAMES))): + if "hip_motor_2_chassis_joint" in name: + angle = INIT_MOTOR_ANGLES[i] + HIP_JOINT_OFFSET + elif "upper_leg_2_hip_motor_joint" in name: + angle = INIT_MOTOR_ANGLES[i] + UPPER_LEG_JOINT_OFFSET + elif "lower_leg_2_upper_leg_joint" in name: + angle = INIT_MOTOR_ANGLES[i] + KNEE_JOINT_OFFSET + else: + raise ValueError("The name %s is not recognized as a motor joint." % + name) + self._pybullet_client.resetJointState( + self.quadruped, self._joint_name_to_id[name], angle, targetVelocity=0) + + def GetURDFFile(self): + return os.path.join(self._urdf_root, "laikago/" + self._urdf_filename) + + def _BuildUrdfIds(self): + """Build the link Ids from its name in the URDF file. + + Raises: + ValueError: Unknown category of the joint name. + """ + num_joints = self._pybullet_client.getNumJoints(self.quadruped) + self._chassis_link_ids = [-1] + self._leg_link_ids = [] + self._motor_link_ids = [] + self._knee_link_ids = [] + self._foot_link_ids = [] + + for i in range(num_joints): + joint_info = self._pybullet_client.getJointInfo(self.quadruped, i) + joint_name = joint_info[1].decode("UTF-8") + joint_id = self._joint_name_to_id[joint_name] + if CHASSIS_NAME_PATTERN.match(joint_name): + self._chassis_link_ids.append(joint_id) + elif MOTOR_NAME_PATTERN.match(joint_name): + self._motor_link_ids.append(joint_id) + # We either treat the lower leg or the toe as the foot link, depending on + # the urdf version used. + elif KNEE_NAME_PATTERN.match(joint_name): + self._knee_link_ids.append(joint_id) + elif TOE_NAME_PATTERN.match(joint_name): + assert self._urdf_filename == URDF_WITH_TOES + self._foot_link_ids.append(joint_id) + else: + raise ValueError("Unknown category of joint %s" % joint_name) + + self._leg_link_ids.extend(self._knee_link_ids) + self._leg_link_ids.extend(self._foot_link_ids) + + if self._urdf_filename == URDF_NO_TOES: + self._foot_link_ids.extend(self._knee_link_ids) + + assert len(self._foot_link_ids) == NUM_LEGS + self._chassis_link_ids.sort() + self._motor_link_ids.sort() + self._knee_link_ids.sort() + self._foot_link_ids.sort() + self._leg_link_ids.sort() + + return + + def _GetMotorNames(self): + return MOTOR_NAMES + + def _GetDefaultInitPosition(self): + if self._on_rack: + return INIT_RACK_POSITION + else: + return INIT_POSITION + + def _GetDefaultInitOrientation(self): + # The Laikago URDF assumes the initial pose of heading towards z axis, + # and belly towards y axis. The following transformation is to transform + # the Laikago initial orientation to our commonly used orientation: heading + # towards -x direction, and z axis is the up direction. + if self._urdf_filename == URDF_WITH_TOES: + return [0, 0, 0, 1] + else: + return transformations.quaternion_from_euler( + ai=math.pi / 2.0, aj=0, ak=math.pi / 2.0, axes="sxyz") + + def GetDefaultInitPosition(self): + """Get default initial base position.""" + return self._GetDefaultInitPosition() + + def GetDefaultInitOrientation(self): + """Get default initial base orientation.""" + return self._GetDefaultInitOrientation() + + def GetDefaultInitJointPose(self): + """Get default initial joint pose.""" + joint_pose = (INIT_MOTOR_ANGLES + JOINT_OFFSETS) * JOINT_DIRECTIONS + return joint_pose + + def ApplyAction(self, motor_commands, motor_control_mode=None): + """Clips and then apply the motor commands using the motor model. + + Args: + motor_commands: np.array. Can be motor angles, torques, hybrid commands, + or motor pwms (for Minitaur only).N + motor_control_mode: A MotorControlMode enum. + """ + if self._enable_clip_motor_commands: + motor_commands = self._ClipMotorCommands(motor_commands) + + super(Laikago, self).ApplyAction(motor_commands, motor_control_mode) + return + + def _ClipMotorCommands(self, motor_commands): + """Clips motor commands. + + Args: + motor_commands: np.array. Can be motor angles, torques, hybrid commands, + or motor pwms (for Minitaur only). + + Returns: + Clipped motor commands. + """ + + # clamp the motor command by the joint limit, in case weired things happens + max_angle_change = MAX_MOTOR_ANGLE_CHANGE_PER_STEP + current_motor_angles = self.GetMotorAngles() + motor_commands = np.clip(motor_commands, + current_motor_angles - max_angle_change, + current_motor_angles + max_angle_change) + return motor_commands + + @classmethod + def GetConstants(cls): + del cls + return laikago_constants + + # The following functions are added for the migration purpose. Will be removed + # after the migration is complete. + + @property + def robot_id(self): + return self.quadruped + + @property + def base_position(self): + return self.GetBasePosition() + + @property + def base_roll_pitch_yaw(self): + return self.GetTrueBaseRollPitchYaw() + + @property + def timestamp(self): + return self.GetTimeSinceReset() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_constants.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_constants.py new file mode 100644 index 000000000..d07e73071 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_constants.py @@ -0,0 +1,120 @@ +# Lint as: python3 +"""Defines the laikago robot related constants and URDF specs.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import gin + +URDF_PATH = "laikago/laikago_toes_zup.urdf" + +NUM_MOTORS = 12 +NUM_LEGS = 4 +MOTORS_PER_LEG = 3 + +INIT_RACK_POSITION = [0, 0, 1] +INIT_POSITION = [0, 0, 0.48] + +# Will be default to (0, 0, 0, 1) once the new laikago_toes_zup.urdf checked in. +INIT_ORIENTATION = [0, 0, 0, 1] + +# Can be different from the motors, although for laikago they are the same list. +JOINT_NAMES = ( + # front right leg + "FR_hip_motor_2_chassis_joint", + "FR_upper_leg_2_hip_motor_joint", + "FR_lower_leg_2_upper_leg_joint", + # front left leg + "FL_hip_motor_2_chassis_joint", + "FL_upper_leg_2_hip_motor_joint", + "FL_lower_leg_2_upper_leg_joint", + # rear right leg + "RR_hip_motor_2_chassis_joint", + "RR_upper_leg_2_hip_motor_joint", + "RR_lower_leg_2_upper_leg_joint", + # rear left leg + "RL_hip_motor_2_chassis_joint", + "RL_upper_leg_2_hip_motor_joint", + "RL_lower_leg_2_upper_leg_joint", +) + +INIT_ABDUCTION_ANGLE = 0 +INIT_HIP_ANGLE = 0.67 +INIT_KNEE_ANGLE = -1.25 + +# Note this matches the Laikago SDK/control convention, but is different from +# URDF's internal joint angles which needs to be computed using the joint +# offsets and directions. The conversion formula is (sdk_joint_angle + offset) * +# joint direction. +INIT_JOINT_ANGLES = collections.OrderedDict( + zip(JOINT_NAMES, + (INIT_ABDUCTION_ANGLE, INIT_HIP_ANGLE, INIT_KNEE_ANGLE) * NUM_LEGS)) + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_DIRECTIONS = collections.OrderedDict( + zip(JOINT_NAMES, (-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1))) + +HIP_JOINT_OFFSET = 0.0 +UPPER_LEG_JOINT_OFFSET = -0.6 +KNEE_JOINT_OFFSET = 0.66 + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_OFFSETS = collections.OrderedDict( + zip(JOINT_NAMES, + [HIP_JOINT_OFFSET, UPPER_LEG_JOINT_OFFSET, KNEE_JOINT_OFFSET] * + NUM_LEGS)) + +LEG_NAMES = ( + "front_right", + "front_left", + "rear_right", + "rear_left", +) + +LEG_ORDER = ( + "front_right", + "front_left", + "back_right", + "back_left", +) + +END_EFFECTOR_NAMES = ( + "jtoeFR", + "jtoeFL", + "jtoeRR", + "jtoeRL", +) + +MOTOR_NAMES = JOINT_NAMES +MOTOR_GROUP = collections.OrderedDict(( + (LEG_NAMES[0], JOINT_NAMES[0:3]), + (LEG_NAMES[1], JOINT_NAMES[3:6]), + (LEG_NAMES[2], JOINT_NAMES[6:9]), + (LEG_NAMES[3], JOINT_NAMES[9:12]), +)) + +# Regulates the joint angle change when in position control mode. +MAX_MOTOR_ANGLE_CHANGE_PER_STEP = 0.12 + +# The hip joint location in the CoM frame. +HIP_POSITIONS = collections.OrderedDict(( + (LEG_NAMES[0], (0.21, -0.1157, 0)), + (LEG_NAMES[1], (0.21, 0.1157, 0)), + (LEG_NAMES[2], (-0.21, -0.1157, 0)), + (LEG_NAMES[3], (-0.21, 0.1157, 0)), +)) + +# Add the gin constants to be used for gin binding in config. Append "LAIKAGO_" +# for unique binding names. +gin.constant("laikago_constants.LAIKAGO_NUM_MOTORS", NUM_MOTORS) +gin.constant("laikago_constants.LAIKAGO_URDF_PATH", URDF_PATH) +gin.constant("laikago_constants.LAIKAGO_INIT_POSITION", INIT_POSITION) +gin.constant("laikago_constants.LAIKAGO_INIT_ORIENTATION", INIT_ORIENTATION) +gin.constant("laikago_constants.LAIKAGO_INIT_JOINT_ANGLES", INIT_JOINT_ANGLES) +gin.constant("laikago_constants.LAIKAGO_JOINT_DIRECTIONS", JOINT_DIRECTIONS) +gin.constant("laikago_constants.LAIKAGO_JOINT_OFFSETS", JOINT_OFFSETS) +gin.constant("laikago_constants.LAIKAGO_MOTOR_NAMES", MOTOR_NAMES) +gin.constant("laikago_constants.LAIKAGO_END_EFFECTOR_NAMES", END_EFFECTOR_NAMES) +gin.constant("laikago_constants.LAIKAGO_MOTOR_GROUP", MOTOR_GROUP) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface.proto b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface.proto new file mode 100644 index 000000000..3e2a31a97 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface.proto @@ -0,0 +1,181 @@ +syntax = "proto3"; + +package minitaur_fluxworks.control; + +import "timestamp.proto"; +import "vector.proto"; + +// A general motor command. +message MotorCommand { + // The unique motor id. + uint32 motor_id = 1; + + // The motor angle. + float position = 2; + + float position_gain = 3; + + // The motor velocity. + float velocity = 4; + float velocity_gain = 5; + + // The feed forward torque. + float torque = 6; +} + +// LED command for the foot. +message Led { + uint32 leg_id = 1; + uint32 r = 2; + uint32 g = 3; + uint32 b = 4; +} + +// The message type for Laikago's motor command. +message LaikagoCommand { + google.protobuf.Timestamp timestamp = 1; + enum ControlMode { + CONTROL_MODE_UNSPECIFIED = 0; + CONTROL_MODE_POSITION = 1; + CONTROL_MODE_TORQUE = 2; + CONTROL_MODE_HYBRID = 3; + } + ControlMode control_mode = 2; + repeated MotorCommand motor_command = 3; + repeated Led led = 4; +} + +// Empty message just to request a state from the control server. +message LaikagoStateRequest {} + +message Imu { + robotics.messages.Vector4f quaternion = 1; + + // The unit is rad/s + robotics.messages.Vector3f gyroscope = 2; + + // The unit is m/s^2 + robotics.messages.Vector3f acceleration = 3; + + // The unit is rad + robotics.messages.Vector3f rpy = 4; + + // The IMU temperature. + float temperature = 5; +} + +message MotorState { + uint32 motor_id = 1; + uint32 mode = 2; + + float position = 3; + // Position/Velocity gains cannot be read from the motor. We just save the + // last used value. + float position_gain = 4; + float velocity = 5; + float velocity_gain = 6; + float torque = 7; + float temperature = 8; +} + +message ContactState { + uint32 leg_id = 1; + + // Contact force is measured in one dimension for Laikago. + float force = 2; + + // The contact force measurement direction. + robotics.messages.Vector3f axis = 3; +} + +// The message type for Laikago's low level state. +message LaikagoState { + google.protobuf.Timestamp timestamp = 1; + uint32 control_level = 2; + Imu imu = 3; + repeated MotorState motor_state = 4; + repeated ContactState contact_state = 5; + // The microcontroller_time is millis. + uint32 microcontroller_time_millis = 6; + bytes wireless_remote = 7; + uint32 crc = 8; +} + +message LaikagoCommandState { + LaikagoCommand command = 1; + LaikagoState state = 2; +} + +// The optional gRPC interface for Laikago control. +service LaikagoControlGrpcInterface { + // Sends the low level control command and receives a state. + rpc SendCommand(LaikagoCommand) returns (LaikagoState) {} + + // Receives a robot state without sending motor commands. + rpc GetState(LaikagoStateRequest) returns (LaikagoState) {} +} + +// Reserved for Laikago's high level command. +message LaikagoHighLevelCommand { + google.protobuf.Timestamp timestamp = 1; + uint32 control_level = 2; + + // 1 for standing and 2 for walking. + uint32 control_mode = 3; + + // The normalized speed tuple (x, y, \omega_z) + robotics.messages.Vector3f walk_speed = 4; + + float body_height = 5; + float foot_clearance_height = 6; + + // The target roll, pitch, yaw of the body in the stand mode. + robotics.messages.Vector3f rpy = 7; +} + +// Reserved for Laikago's high level status. +message LaikagoHighLevelState { + google.protobuf.Timestamp timestamp = 1; + uint32 control_level = 2; + + // 1 for standing and 2 for walking. + uint32 control_mode = 3; + Imu imu = 4; + + // The normalized speed tuple (x, y, \omega_z) + robotics.messages.Vector3f walk_speed = 5; + + // In stand mode. + float body_height = 8; + float up_down_speed = 9; + + // The com position estimation. Will drift in x-y plane. + robotics.messages.Vector3f com_position = 10; + repeated robotics.messages.Vector3f foot_position_to_com = 11; + repeated robotics.messages.Vector3f foot_velocity_to_com = 12; + repeated ContactState contact_state = 13; + // The microcontroller_time is millis. + uint32 microcontroller_time_millis = 14; + // Bytes 4-7: slider_lx (side step speed); Bytes 8-11: slider_rx (twisting + // speed); Bytes 12-15: -slider_ry. Bytes 16-19: (slider_r + 1) / 2; Bytes + // 20-23: -slider_ly (forward/backward speed). Each float number (4 bytes) are + // packed using big endian convention. + bytes wireless_remote = 15; + uint32 crc = 16; +} + +message LaikagoHighLevelStateRequest {} + +message LaikagoHighLevelCommandState { + LaikagoHighLevelCommand command = 1; + LaikagoHighLevelState state = 2; +} + +// The optional gRPC interface for Laikago control. +service LaikagoHighLevelControlGrpcInterface { + // Sends the high level control command and receives a state. + rpc SendCommand(LaikagoHighLevelCommand) returns (LaikagoHighLevelState) {} + + // Requests a state without sending commands. + rpc GetState(LaikagoHighLevelStateRequest) returns (LaikagoHighLevelState) {} +} diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface_pb2.py new file mode 100644 index 000000000..7d67f55cc --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_interface_pb2.py @@ -0,0 +1,1040 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: laikago_interface.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from pybullet_envs.minitaur.robots import timestamp_pb2 as timestamp__pb2 +from pybullet_envs.minitaur.robots import vector_pb2 as vector__pb2 + + + +DESCRIPTOR = _descriptor.FileDescriptor( + name='laikago_interface.proto', + package='minitaur_fluxworks.control', + syntax='proto3', + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\x17laikago_interface.proto\x12\x1aminitaur_fluxworks.control\x1a\x0ftimestamp.proto\x1a\x0cvector.proto\"\x82\x01\n\x0cMotorCommand\x12\x10\n\x08motor_id\x18\x01 \x01(\r\x12\x10\n\x08position\x18\x02 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+ + +_LAIKAGOCOMMAND_CONTROLMODE = _descriptor.EnumDescriptor( + name='ControlMode', + full_name='minitaur_fluxworks.control.LaikagoCommand.ControlMode', + filename=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key, + values=[ + _descriptor.EnumValueDescriptor( + name='CONTROL_MODE_UNSPECIFIED', index=0, number=0, + serialized_options=None, + type=None, + create_key=_descriptor._internal_create_key), + _descriptor.EnumValueDescriptor( + name='CONTROL_MODE_POSITION', index=1, number=1, + serialized_options=None, + type=None, + create_key=_descriptor._internal_create_key), + _descriptor.EnumValueDescriptor( + name='CONTROL_MODE_TORQUE', index=2, number=2, + serialized_options=None, + type=None, + create_key=_descriptor._internal_create_key), + _descriptor.EnumValueDescriptor( + name='CONTROL_MODE_HYBRID', index=3, number=3, + serialized_options=None, + type=None, + create_key=_descriptor._internal_create_key), + ], + containing_type=None, + serialized_options=None, + serialized_start=530, + serialized_end=650, +) +_sym_db.RegisterEnumDescriptor(_LAIKAGOCOMMAND_CONTROLMODE) + + +_MOTORCOMMAND = _descriptor.Descriptor( + name='MotorCommand', + full_name='minitaur_fluxworks.control.MotorCommand', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='motor_id', full_name='minitaur_fluxworks.control.MotorCommand.motor_id', index=0, + number=1, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='position', full_name='minitaur_fluxworks.control.MotorCommand.position', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='position_gain', full_name='minitaur_fluxworks.control.MotorCommand.position_gain', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='velocity', full_name='minitaur_fluxworks.control.MotorCommand.velocity', index=3, + number=4, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='velocity_gain', full_name='minitaur_fluxworks.control.MotorCommand.velocity_gain', index=4, + number=5, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='torque', full_name='minitaur_fluxworks.control.MotorCommand.torque', index=5, + number=6, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=87, + serialized_end=217, +) + + +_LED = _descriptor.Descriptor( + name='Led', + full_name='minitaur_fluxworks.control.Led', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='leg_id', full_name='minitaur_fluxworks.control.Led.leg_id', index=0, + number=1, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='r', full_name='minitaur_fluxworks.control.Led.r', index=1, + number=2, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='g', full_name='minitaur_fluxworks.control.Led.g', index=2, + number=3, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='b', full_name='minitaur_fluxworks.control.Led.b', index=3, + number=4, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=219, + serialized_end=273, +) + + +_LAIKAGOCOMMAND = _descriptor.Descriptor( + name='LaikagoCommand', + full_name='minitaur_fluxworks.control.LaikagoCommand', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='timestamp', full_name='minitaur_fluxworks.control.LaikagoCommand.timestamp', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_mode', full_name='minitaur_fluxworks.control.LaikagoCommand.control_mode', index=1, + number=2, type=14, cpp_type=8, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='motor_command', full_name='minitaur_fluxworks.control.LaikagoCommand.motor_command', index=2, + number=3, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='led', full_name='minitaur_fluxworks.control.LaikagoCommand.led', index=3, + number=4, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + _LAIKAGOCOMMAND_CONTROLMODE, + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=276, + serialized_end=650, +) + + +_LAIKAGOSTATEREQUEST = _descriptor.Descriptor( + name='LaikagoStateRequest', + full_name='minitaur_fluxworks.control.LaikagoStateRequest', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=652, + serialized_end=673, +) + + +_IMU = _descriptor.Descriptor( + name='Imu', + full_name='minitaur_fluxworks.control.Imu', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='quaternion', full_name='minitaur_fluxworks.control.Imu.quaternion', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='gyroscope', full_name='minitaur_fluxworks.control.Imu.gyroscope', index=1, + number=2, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='acceleration', full_name='minitaur_fluxworks.control.Imu.acceleration', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='rpy', full_name='minitaur_fluxworks.control.Imu.rpy', index=3, + number=4, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='temperature', full_name='minitaur_fluxworks.control.Imu.temperature', index=4, + number=5, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=676, + serialized_end=892, +) + + +_MOTORSTATE = _descriptor.Descriptor( + name='MotorState', + full_name='minitaur_fluxworks.control.MotorState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='motor_id', full_name='minitaur_fluxworks.control.MotorState.motor_id', index=0, + number=1, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='mode', full_name='minitaur_fluxworks.control.MotorState.mode', index=1, + number=2, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='position', full_name='minitaur_fluxworks.control.MotorState.position', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='position_gain', full_name='minitaur_fluxworks.control.MotorState.position_gain', index=3, + number=4, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='velocity', full_name='minitaur_fluxworks.control.MotorState.velocity', index=4, + number=5, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='velocity_gain', full_name='minitaur_fluxworks.control.MotorState.velocity_gain', index=5, + number=6, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='torque', full_name='minitaur_fluxworks.control.MotorState.torque', index=6, + number=7, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='temperature', full_name='minitaur_fluxworks.control.MotorState.temperature', index=7, + number=8, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=895, + serialized_end=1058, +) + + +_CONTACTSTATE = _descriptor.Descriptor( + name='ContactState', + full_name='minitaur_fluxworks.control.ContactState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='leg_id', full_name='minitaur_fluxworks.control.ContactState.leg_id', index=0, + number=1, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='force', full_name='minitaur_fluxworks.control.ContactState.force', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='axis', full_name='minitaur_fluxworks.control.ContactState.axis', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=1060, + serialized_end=1148, +) + + +_LAIKAGOSTATE = _descriptor.Descriptor( + name='LaikagoState', + full_name='minitaur_fluxworks.control.LaikagoState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='timestamp', full_name='minitaur_fluxworks.control.LaikagoState.timestamp', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_level', full_name='minitaur_fluxworks.control.LaikagoState.control_level', index=1, + number=2, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='imu', full_name='minitaur_fluxworks.control.LaikagoState.imu', index=2, + number=3, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='motor_state', full_name='minitaur_fluxworks.control.LaikagoState.motor_state', index=3, + number=4, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='contact_state', full_name='minitaur_fluxworks.control.LaikagoState.contact_state', index=4, + number=5, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='microcontroller_time_millis', full_name='minitaur_fluxworks.control.LaikagoState.microcontroller_time_millis', index=5, + number=6, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='wireless_remote', full_name='minitaur_fluxworks.control.LaikagoState.wireless_remote', index=6, + number=7, type=12, cpp_type=9, label=1, + has_default_value=False, default_value=b"", + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='crc', full_name='minitaur_fluxworks.control.LaikagoState.crc', index=7, + number=8, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=1151, + serialized_end=1482, +) + + +_LAIKAGOCOMMANDSTATE = _descriptor.Descriptor( + name='LaikagoCommandState', + full_name='minitaur_fluxworks.control.LaikagoCommandState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='command', full_name='minitaur_fluxworks.control.LaikagoCommandState.command', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='state', full_name='minitaur_fluxworks.control.LaikagoCommandState.state', index=1, + number=2, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=1485, + serialized_end=1624, +) + + +_LAIKAGOHIGHLEVELCOMMAND = _descriptor.Descriptor( + name='LaikagoHighLevelCommand', + full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='timestamp', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.timestamp', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_level', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.control_level', index=1, + number=2, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_mode', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.control_mode', index=2, + number=3, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='walk_speed', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.walk_speed', index=3, + number=4, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='body_height', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.body_height', index=4, + number=5, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='foot_clearance_height', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.foot_clearance_height', index=5, + number=6, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='rpy', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommand.rpy', index=6, + number=7, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=1627, + serialized_end=1887, +) + + +_LAIKAGOHIGHLEVELSTATE = _descriptor.Descriptor( + name='LaikagoHighLevelState', + full_name='minitaur_fluxworks.control.LaikagoHighLevelState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='timestamp', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.timestamp', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_level', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.control_level', index=1, + number=2, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='control_mode', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.control_mode', index=2, + number=3, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='imu', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.imu', index=3, + number=4, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='walk_speed', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.walk_speed', index=4, + number=5, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='body_height', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.body_height', index=5, + number=8, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='up_down_speed', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.up_down_speed', index=6, + number=9, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='com_position', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.com_position', index=7, + number=10, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='foot_position_to_com', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.foot_position_to_com', index=8, + number=11, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='foot_velocity_to_com', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.foot_velocity_to_com', index=9, + number=12, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='contact_state', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.contact_state', index=10, + number=13, type=11, cpp_type=10, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='microcontroller_time_millis', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.microcontroller_time_millis', index=11, + number=14, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='wireless_remote', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.wireless_remote', index=12, + number=15, type=12, cpp_type=9, label=1, + has_default_value=False, default_value=b"", + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='crc', full_name='minitaur_fluxworks.control.LaikagoHighLevelState.crc', index=13, + number=16, type=13, cpp_type=3, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=1890, + serialized_end=2453, +) + + +_LAIKAGOHIGHLEVELSTATEREQUEST = _descriptor.Descriptor( + name='LaikagoHighLevelStateRequest', + full_name='minitaur_fluxworks.control.LaikagoHighLevelStateRequest', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=2455, + serialized_end=2485, +) + + +_LAIKAGOHIGHLEVELCOMMANDSTATE = _descriptor.Descriptor( + name='LaikagoHighLevelCommandState', + full_name='minitaur_fluxworks.control.LaikagoHighLevelCommandState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='command', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommandState.command', index=0, + number=1, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='state', full_name='minitaur_fluxworks.control.LaikagoHighLevelCommandState.state', index=1, + number=2, type=11, cpp_type=10, label=1, + has_default_value=False, default_value=None, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=2488, + serialized_end=2654, +) + +_LAIKAGOCOMMAND.fields_by_name['timestamp'].message_type = timestamp__pb2._TIMESTAMP +_LAIKAGOCOMMAND.fields_by_name['control_mode'].enum_type = _LAIKAGOCOMMAND_CONTROLMODE +_LAIKAGOCOMMAND.fields_by_name['motor_command'].message_type = _MOTORCOMMAND +_LAIKAGOCOMMAND.fields_by_name['led'].message_type = _LED +_LAIKAGOCOMMAND_CONTROLMODE.containing_type = _LAIKAGOCOMMAND +_IMU.fields_by_name['quaternion'].message_type = vector__pb2._VECTOR4F +_IMU.fields_by_name['gyroscope'].message_type = vector__pb2._VECTOR3F +_IMU.fields_by_name['acceleration'].message_type = vector__pb2._VECTOR3F +_IMU.fields_by_name['rpy'].message_type = vector__pb2._VECTOR3F +_CONTACTSTATE.fields_by_name['axis'].message_type = vector__pb2._VECTOR3F +_LAIKAGOSTATE.fields_by_name['timestamp'].message_type = timestamp__pb2._TIMESTAMP +_LAIKAGOSTATE.fields_by_name['imu'].message_type = _IMU +_LAIKAGOSTATE.fields_by_name['motor_state'].message_type = _MOTORSTATE +_LAIKAGOSTATE.fields_by_name['contact_state'].message_type = _CONTACTSTATE +_LAIKAGOCOMMANDSTATE.fields_by_name['command'].message_type = _LAIKAGOCOMMAND +_LAIKAGOCOMMANDSTATE.fields_by_name['state'].message_type = _LAIKAGOSTATE +_LAIKAGOHIGHLEVELCOMMAND.fields_by_name['timestamp'].message_type = timestamp__pb2._TIMESTAMP +_LAIKAGOHIGHLEVELCOMMAND.fields_by_name['walk_speed'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELCOMMAND.fields_by_name['rpy'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELSTATE.fields_by_name['timestamp'].message_type = timestamp__pb2._TIMESTAMP +_LAIKAGOHIGHLEVELSTATE.fields_by_name['imu'].message_type = _IMU +_LAIKAGOHIGHLEVELSTATE.fields_by_name['walk_speed'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELSTATE.fields_by_name['com_position'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELSTATE.fields_by_name['foot_position_to_com'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELSTATE.fields_by_name['foot_velocity_to_com'].message_type = vector__pb2._VECTOR3F +_LAIKAGOHIGHLEVELSTATE.fields_by_name['contact_state'].message_type = _CONTACTSTATE +_LAIKAGOHIGHLEVELCOMMANDSTATE.fields_by_name['command'].message_type = _LAIKAGOHIGHLEVELCOMMAND +_LAIKAGOHIGHLEVELCOMMANDSTATE.fields_by_name['state'].message_type = _LAIKAGOHIGHLEVELSTATE +DESCRIPTOR.message_types_by_name['MotorCommand'] = _MOTORCOMMAND +DESCRIPTOR.message_types_by_name['Led'] = _LED +DESCRIPTOR.message_types_by_name['LaikagoCommand'] = _LAIKAGOCOMMAND +DESCRIPTOR.message_types_by_name['LaikagoStateRequest'] = _LAIKAGOSTATEREQUEST +DESCRIPTOR.message_types_by_name['Imu'] = _IMU +DESCRIPTOR.message_types_by_name['MotorState'] = _MOTORSTATE +DESCRIPTOR.message_types_by_name['ContactState'] = _CONTACTSTATE +DESCRIPTOR.message_types_by_name['LaikagoState'] = _LAIKAGOSTATE +DESCRIPTOR.message_types_by_name['LaikagoCommandState'] = _LAIKAGOCOMMANDSTATE +DESCRIPTOR.message_types_by_name['LaikagoHighLevelCommand'] = _LAIKAGOHIGHLEVELCOMMAND +DESCRIPTOR.message_types_by_name['LaikagoHighLevelState'] = _LAIKAGOHIGHLEVELSTATE +DESCRIPTOR.message_types_by_name['LaikagoHighLevelStateRequest'] = _LAIKAGOHIGHLEVELSTATEREQUEST +DESCRIPTOR.message_types_by_name['LaikagoHighLevelCommandState'] = _LAIKAGOHIGHLEVELCOMMANDSTATE +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +MotorCommand = _reflection.GeneratedProtocolMessageType('MotorCommand', (_message.Message,), { + 'DESCRIPTOR' : _MOTORCOMMAND, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.MotorCommand) + }) +_sym_db.RegisterMessage(MotorCommand) + +Led = _reflection.GeneratedProtocolMessageType('Led', (_message.Message,), { + 'DESCRIPTOR' : _LED, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.Led) + }) +_sym_db.RegisterMessage(Led) + +LaikagoCommand = _reflection.GeneratedProtocolMessageType('LaikagoCommand', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOCOMMAND, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoCommand) + }) +_sym_db.RegisterMessage(LaikagoCommand) + +LaikagoStateRequest = _reflection.GeneratedProtocolMessageType('LaikagoStateRequest', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOSTATEREQUEST, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoStateRequest) + }) +_sym_db.RegisterMessage(LaikagoStateRequest) + +Imu = _reflection.GeneratedProtocolMessageType('Imu', (_message.Message,), { + 'DESCRIPTOR' : _IMU, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.Imu) + }) +_sym_db.RegisterMessage(Imu) + +MotorState = _reflection.GeneratedProtocolMessageType('MotorState', (_message.Message,), { + 'DESCRIPTOR' : _MOTORSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.MotorState) + }) +_sym_db.RegisterMessage(MotorState) + +ContactState = _reflection.GeneratedProtocolMessageType('ContactState', (_message.Message,), { + 'DESCRIPTOR' : _CONTACTSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.ContactState) + }) +_sym_db.RegisterMessage(ContactState) + +LaikagoState = _reflection.GeneratedProtocolMessageType('LaikagoState', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoState) + }) +_sym_db.RegisterMessage(LaikagoState) + +LaikagoCommandState = _reflection.GeneratedProtocolMessageType('LaikagoCommandState', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOCOMMANDSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoCommandState) + }) +_sym_db.RegisterMessage(LaikagoCommandState) + +LaikagoHighLevelCommand = _reflection.GeneratedProtocolMessageType('LaikagoHighLevelCommand', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOHIGHLEVELCOMMAND, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoHighLevelCommand) + }) +_sym_db.RegisterMessage(LaikagoHighLevelCommand) + +LaikagoHighLevelState = _reflection.GeneratedProtocolMessageType('LaikagoHighLevelState', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOHIGHLEVELSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoHighLevelState) + }) +_sym_db.RegisterMessage(LaikagoHighLevelState) + +LaikagoHighLevelStateRequest = _reflection.GeneratedProtocolMessageType('LaikagoHighLevelStateRequest', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOHIGHLEVELSTATEREQUEST, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoHighLevelStateRequest) + }) +_sym_db.RegisterMessage(LaikagoHighLevelStateRequest) + +LaikagoHighLevelCommandState = _reflection.GeneratedProtocolMessageType('LaikagoHighLevelCommandState', (_message.Message,), { + 'DESCRIPTOR' : _LAIKAGOHIGHLEVELCOMMANDSTATE, + '__module__' : 'laikago_interface_pb2' + # @@protoc_insertion_point(class_scope:minitaur_fluxworks.control.LaikagoHighLevelCommandState) + }) +_sym_db.RegisterMessage(LaikagoHighLevelCommandState) + + + +_LAIKAGOCONTROLGRPCINTERFACE = _descriptor.ServiceDescriptor( + name='LaikagoControlGrpcInterface', + full_name='minitaur_fluxworks.control.LaikagoControlGrpcInterface', + file=DESCRIPTOR, + index=0, + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_start=2657, + serialized_end=2894, + methods=[ + _descriptor.MethodDescriptor( + name='SendCommand', + full_name='minitaur_fluxworks.control.LaikagoControlGrpcInterface.SendCommand', + index=0, + containing_service=None, + input_type=_LAIKAGOCOMMAND, + output_type=_LAIKAGOSTATE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + _descriptor.MethodDescriptor( + name='GetState', + full_name='minitaur_fluxworks.control.LaikagoControlGrpcInterface.GetState', + index=1, + containing_service=None, + input_type=_LAIKAGOSTATEREQUEST, + output_type=_LAIKAGOSTATE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), +]) +_sym_db.RegisterServiceDescriptor(_LAIKAGOCONTROLGRPCINTERFACE) + +DESCRIPTOR.services_by_name['LaikagoControlGrpcInterface'] = _LAIKAGOCONTROLGRPCINTERFACE + + +_LAIKAGOHIGHLEVELCONTROLGRPCINTERFACE = _descriptor.ServiceDescriptor( + name='LaikagoHighLevelControlGrpcInterface', + full_name='minitaur_fluxworks.control.LaikagoHighLevelControlGrpcInterface', + file=DESCRIPTOR, + index=1, + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_start=2897, + serialized_end=3179, + methods=[ + _descriptor.MethodDescriptor( + name='SendCommand', + full_name='minitaur_fluxworks.control.LaikagoHighLevelControlGrpcInterface.SendCommand', + index=0, + containing_service=None, + input_type=_LAIKAGOHIGHLEVELCOMMAND, + output_type=_LAIKAGOHIGHLEVELSTATE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), + _descriptor.MethodDescriptor( + name='GetState', + full_name='minitaur_fluxworks.control.LaikagoHighLevelControlGrpcInterface.GetState', + index=1, + containing_service=None, + input_type=_LAIKAGOHIGHLEVELSTATEREQUEST, + output_type=_LAIKAGOHIGHLEVELSTATE, + serialized_options=None, + create_key=_descriptor._internal_create_key, + ), +]) +_sym_db.RegisterServiceDescriptor(_LAIKAGOHIGHLEVELCONTROLGRPCINTERFACE) + +DESCRIPTOR.services_by_name['LaikagoHighLevelControlGrpcInterface'] = _LAIKAGOHIGHLEVELCONTROLGRPCINTERFACE + +# @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_kinematic_constants.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_kinematic_constants.py new file mode 100644 index 000000000..eae1662da --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_kinematic_constants.py @@ -0,0 +1,106 @@ +# Lint as: python3 +"""Defines the LaikagoKinematic robot related constants and URDF specs.""" + +import collections +import gin + +LAIKAGO_KINEMATIC_URDF_PATH = "robotics/reinforcement_learning/minitaur/robots/data/urdf/laikago/laikago_toes_zup_kinematic.urdf" +INIT_POSITION = (0, 0, 0.5) +INIT_ORIENTATION_QUAT = (0, 0, 0, 1) +INIT_ORIENTATION_RPY = (0, 0, 0) + +NUM_LEGS = 4 +# TODO(b/153405332): Use link name instead of joint name to identify the +# base link. +BASE_NAMES = ("kinematic_drive_joint_th",) + +JOINT_NAMES = ( + "kinematic_drive_joint_x", + "kinematic_drive_joint_y", + "kinematic_drive_joint_th", + # front right leg + "FR_hip_motor_2_chassis_joint", + "FR_upper_leg_2_hip_motor_joint", + "FR_lower_leg_2_upper_leg_joint", + # front left leg + "FL_hip_motor_2_chassis_joint", + "FL_upper_leg_2_hip_motor_joint", + "FL_lower_leg_2_upper_leg_joint", + # rear right leg + "RR_hip_motor_2_chassis_joint", + "RR_upper_leg_2_hip_motor_joint", + "RR_lower_leg_2_upper_leg_joint", + # rear left leg + "RL_hip_motor_2_chassis_joint", + "RL_upper_leg_2_hip_motor_joint", + "RL_lower_leg_2_upper_leg_joint", +) + +# A default joint pose where the arm is tucked near the base, and head looking +# forward. +INIT_ABDUCTION_ANGLE = 0 +INIT_HIP_ANGLE = 0.67 +INIT_KNEE_ANGLE = -1.25 + +# Note this matches the Laikago SDK/control convention, but is different from +# URDF's internal joint angles which needs to be computed using the joint +# offsets and directions. The conversion formula is (sdk_joint_angle + offset) * +# joint direction. +INIT_JOINT_ANGLES = collections.OrderedDict( + zip(JOINT_NAMES, (0, 0, 0) + + (INIT_ABDUCTION_ANGLE, INIT_HIP_ANGLE, INIT_KNEE_ANGLE) * NUM_LEGS)) + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_DIRECTIONS = collections.OrderedDict( + zip(JOINT_NAMES, (1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1))) + +HIP_JOINT_OFFSET = 0.0 +UPPER_LEG_JOINT_OFFSET = -0.6 +KNEE_JOINT_OFFSET = 0.66 + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_OFFSETS = collections.OrderedDict( + zip(JOINT_NAMES, [0, 0, 0] + + [HIP_JOINT_OFFSET, UPPER_LEG_JOINT_OFFSET, KNEE_JOINT_OFFSET] * + NUM_LEGS)) + +LEG_NAMES = ( + "front_right", + "front_left", + "rear_right", + "rear_left", +) + +LEG_ORDER = ( + "front_right", + "front_left", + "back_right", + "back_left", +) + +END_EFFECTOR_NAMES = ( + "jtoeFR", + "jtoeFL", + "jtoeRR", + "jtoeRL", +) + +MOTOR_NAMES = JOINT_NAMES +MOTOR_GROUP = collections.OrderedDict((("body_motors", JOINT_NAMES[3:]),)) + +# Add the gin constants to be used for gin binding in config. +gin.constant("laikago_kinematic_constants.LAIKAGO_KINEMATIC_URDF_PATH", + LAIKAGO_KINEMATIC_URDF_PATH) +gin.constant("laikago_kinematic_constants.INIT_POSITION", INIT_POSITION) +gin.constant("laikago_kinematic_constants.INIT_ORIENTATION_QUAT", + INIT_ORIENTATION_QUAT) +gin.constant("laikago_kinematic_constants.INIT_ORIENTATION_RPY", + INIT_ORIENTATION_RPY) +gin.constant("laikago_kinematic_constants.BASE_NAMES", BASE_NAMES) +gin.constant("laikago_kinematic_constants.INIT_JOINT_ANGLES", INIT_JOINT_ANGLES) +gin.constant("laikago_kinematic_constants.JOINT_DIRECTIONS", JOINT_DIRECTIONS) +gin.constant("laikago_kinematic_constants.JOINT_OFFSETS", JOINT_OFFSETS) +gin.constant("laikago_kinematic_constants.MOTOR_NAMES", MOTOR_NAMES) +gin.constant("laikago_kinematic_constants.END_EFFECTOR_NAMES", + END_EFFECTOR_NAMES) +gin.constant("laikago_kinematic_constants.MOTOR_GROUP", MOTOR_GROUP) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_motor.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_motor.py new file mode 100644 index 000000000..a57c0a964 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_motor.py @@ -0,0 +1,149 @@ +"""Motor model for laikago.""" + +import collections +import numpy as np + +from pybullet_envs.minitaur.robots import robot_config + +NUM_MOTORS = 12 + +MOTOR_COMMAND_DIMENSION = 5 + +# These values represent the indices of each field in the motor command tuple +POSITION_INDEX = 0 +POSITION_GAIN_INDEX = 1 +VELOCITY_INDEX = 2 +VELOCITY_GAIN_INDEX = 3 +TORQUE_INDEX = 4 + + +class LaikagoMotorModel(object): + """A simple motor model for Laikago. + + When in POSITION mode, the torque is calculated according to the difference + between current and desired joint angle, as well as the joint velocity. + For more information about PD control, please refer to: + https://en.wikipedia.org/wiki/PID_controller. + + The model supports a HYBRID mode in which each motor command can be a tuple + (desired_motor_angle, position_gain, desired_motor_velocity, velocity_gain, + torque). + + """ + + def __init__(self, + kp=60, + kd=1, + torque_limits=None, + motor_control_mode=robot_config.MotorControlMode.POSITION): + self._kp = kp + self._kd = kd + self._torque_limits = torque_limits + if torque_limits is not None: + if isinstance(torque_limits, (collections.Sequence, np.ndarray)): + self._torque_limits = np.asarray(torque_limits) + else: + self._torque_limits = np.full(NUM_MOTORS, torque_limits) + self._motor_control_mode = motor_control_mode + self._strength_ratios = np.full(NUM_MOTORS, 1) + + def set_strength_ratios(self, ratios): + """Set the strength of each motors relative to the default value. + + Args: + ratios: The relative strength of motor output. A numpy array ranging from + 0.0 to 1.0. + """ + self._strength_ratios = ratios + + def set_motor_gains(self, kp, kd): + """Set the gains of all motors. + + These gains are PD gains for motor positional control. kp is the + proportional gain and kd is the derivative gain. + + Args: + kp: proportional gain of the motors. + kd: derivative gain of the motors. + """ + self._kp = kp + self._kd = kd + + def set_voltage(self, voltage): + pass + + def get_voltage(self): + return 0.0 + + def set_viscous_damping(self, viscous_damping): + pass + + def get_viscous_dampling(self): + return 0.0 + + def convert_to_torque(self, + motor_commands, + motor_angle, + motor_velocity, + true_motor_velocity, + motor_control_mode=None): + """Convert the commands (position control or torque control) to torque. + + Args: + motor_commands: The desired motor angle if the motor is in position + control mode. The pwm signal if the motor is in torque control mode. + motor_angle: The motor angle observed at the current time step. It is + actually the true motor angle observed a few milliseconds ago (pd + latency). + motor_velocity: The motor velocity observed at the current time step, it + is actually the true motor velocity a few milliseconds ago (pd latency). + true_motor_velocity: The true motor velocity. The true velocity is used to + compute back EMF voltage and viscous damping. + motor_control_mode: A MotorControlMode enum. + + Returns: + actual_torque: The torque that needs to be applied to the motor. + observed_torque: The torque observed by the sensor. + """ + del true_motor_velocity + if not motor_control_mode: + motor_control_mode = self._motor_control_mode + + # No processing for motor torques + if motor_control_mode is robot_config.MotorControlMode.TORQUE: + assert len(motor_commands) == NUM_MOTORS + motor_torques = self._strength_ratios * motor_commands + return motor_torques, motor_torques + + desired_motor_angles = None + desired_motor_velocities = None + kp = None + kd = None + additional_torques = np.full(NUM_MOTORS, 0) + if motor_control_mode is robot_config.MotorControlMode.POSITION: + assert len(motor_commands) == NUM_MOTORS + kp = self._kp + kd = self._kd + desired_motor_angles = motor_commands + desired_motor_velocities = np.full(NUM_MOTORS, 0) + elif motor_control_mode is robot_config.MotorControlMode.HYBRID: + # The input should be a 60 dimension vector + assert len(motor_commands) == MOTOR_COMMAND_DIMENSION * NUM_MOTORS + kp = motor_commands[POSITION_GAIN_INDEX::MOTOR_COMMAND_DIMENSION] + kd = motor_commands[VELOCITY_GAIN_INDEX::MOTOR_COMMAND_DIMENSION] + desired_motor_angles = motor_commands[ + POSITION_INDEX::MOTOR_COMMAND_DIMENSION] + desired_motor_velocities = motor_commands[ + VELOCITY_INDEX::MOTOR_COMMAND_DIMENSION] + additional_torques = motor_commands[TORQUE_INDEX::MOTOR_COMMAND_DIMENSION] + motor_torques = -1 * (kp * (motor_angle - desired_motor_angles)) - kd * ( + motor_velocity - desired_motor_velocities) + additional_torques + motor_torques = self._strength_ratios * motor_torques + if self._torque_limits is not None: + if len(self._torque_limits) != len(motor_torques): + raise ValueError( + "Torque limits dimension does not match the number of motors.") + motor_torques = np.clip(motor_torques, -1.0 * self._torque_limits, + self._torque_limits) + + return motor_torques, motor_torques diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_v2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_v2.py new file mode 100644 index 000000000..dcabd05df --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/laikago_v2.py @@ -0,0 +1,34 @@ +# Lint as: python3 +"""Add the new laikago robot.""" +import gin + +from pybullet_envs.minitaur.robots import laikago_constants +from pybullet_envs.minitaur.robots import quadruped_base +from pybullet_envs.minitaur.robots import robot_urdf_loader + + +@gin.configurable +class Laikago(quadruped_base.QuadrupedBase): + """The Laikago class that simulates the quadruped from Unitree.""" + + def _pre_load(self): + """Import the Laikago specific constants. + """ + self._urdf_loader = robot_urdf_loader.RobotUrdfLoader( + pybullet_client=self._pybullet_client, + urdf_path=laikago_constants.URDF_PATH, + enable_self_collision=True, + init_base_position=laikago_constants.INIT_POSITION, + init_base_orientation_quaternion=laikago_constants.INIT_ORIENTATION, + init_joint_angles=laikago_constants.INIT_JOINT_ANGLES, + joint_offsets=laikago_constants.JOINT_OFFSETS, + joint_directions=laikago_constants.JOINT_DIRECTIONS, + motor_names=laikago_constants.MOTOR_NAMES, + end_effector_names=laikago_constants.END_EFFECTOR_NAMES, + user_group=laikago_constants.MOTOR_GROUP, + ) + + @classmethod + def get_constants(cls): + del cls + return laikago_constants diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah.py new file mode 100644 index 000000000..bc8fb1089 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah.py @@ -0,0 +1,133 @@ +"""Pybullet simulation of a vision60 robot.""" +import math +import os + +import gin +import numpy as np + +from pybullet_envs.minitaur.robots import laikago_motor +from pybullet_envs.minitaur.robots import minitaur +from pybullet_envs.minitaur.robots import robot_config + +NUM_MOTORS = 12 +NUM_LEGS = 4 +MOTOR_NAMES = [ + "torso_to_abduct_fl_j", # Left front abduction (hip0). + "abduct_fl_to_thigh_fl_j", # Left front hip (upper0). + "thigh_fl_to_knee_fl_j", # Left front knee (lower0). + "torso_to_abduct_hl_j", # Left rear abduction (hip1). + "abduct_hl_to_thigh_hl_j", # Left rear hip (upper1). + "thigh_hl_to_knee_hl_j", # Left rear knee (lower1). + "torso_to_abduct_fr_j", # Right front abduction (hip2). + "abduct_fr_to_thigh_fr_j", # Right front hip (upper2). + "thigh_fr_to_knee_fr_j", # Right front knee (lower2). + "torso_to_abduct_hr_j", # Right rear abduction (hip3). + "abduct_hr_to_thigh_hr_j", # Right rear hip (upper3). + "thigh_hr_to_knee_hr_j", # Right rear knee (lower3). +] +_DEFAULT_TORQUE_LIMITS = [12, 18, 12] * 4 +INIT_RACK_POSITION = [0, 0, 1.4] +INIT_POSITION = [0, 0, 0.4] +JOINT_DIRECTIONS = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) +HIP_JOINT_OFFSET = 0.0 +UPPER_LEG_JOINT_OFFSET = 0.0 +KNEE_JOINT_OFFSET = 0.0 +DOFS_PER_LEG = 3 +JOINT_OFFSETS = np.array( + [HIP_JOINT_OFFSET, UPPER_LEG_JOINT_OFFSET, KNEE_JOINT_OFFSET] * 4) +PI = math.pi +DEFAULT_ABDUCTION_ANGLE = 0.0 +DEFAULT_HIP_ANGLE = -1.1 +DEFAULT_KNEE_ANGLE = 2.3 +# Bases on the readings from 's default pose. +INIT_MOTOR_ANGLES = [ + DEFAULT_ABDUCTION_ANGLE, DEFAULT_HIP_ANGLE, DEFAULT_KNEE_ANGLE +] * NUM_LEGS +DEFAULT_LOCAL_TOE_POSITIONS = [[0.17, -0.11, -0.16], [0.17, 0.11, -0.16], + [-0.20, -0.11, -0.16], [-0.20, 0.11, -0.16]] + + +@gin.configurable +class MiniCheetah(minitaur.Minitaur): + """A simulation for the mini cheetah robot.""" + + def __init__(self, **kwargs): + if "motor_kp" not in kwargs: + kwargs["motor_kp"] = 100.0 + if "motor_kd" not in kwargs: + kwargs["motor_kd"] = 2.0 + if "motor_torque_limits" not in kwargs: + kwargs["motor_torque_limits"] = _DEFAULT_TORQUE_LIMITS + + # The follwing parameters are fixed for the vision60 robot. + kwargs["num_motors"] = NUM_MOTORS + kwargs["dofs_per_leg"] = DOFS_PER_LEG + kwargs["motor_direction"] = JOINT_DIRECTIONS + kwargs["motor_offset"] = JOINT_OFFSETS + kwargs["motor_overheat_protection"] = False + kwargs["motor_model_class"] = laikago_motor.LaikagoMotorModel + super(MiniCheetah, self).__init__(**kwargs) + + def _LoadRobotURDF(self): + mini_cheetah_urdf_path = "mini_cheetah/mini_cheetah.urdf" + if self._self_collision_enabled: + self.quadruped = self._pybullet_client.loadURDF( + mini_cheetah_urdf_path, + self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation(), + flags=self._pybullet_client.URDF_USE_SELF_COLLISION) + else: + self.quadruped = self._pybullet_client.loadURDF( + mini_cheetah_urdf_path, self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation()) + + def _SettleDownForReset(self, default_motor_angles, reset_time): + self.ReceiveObservation() + for _ in range(500): + self.ApplyAction( + INIT_MOTOR_ANGLES, + motor_control_mode=robot_config.MotorControlMode.POSITION) + self._pybullet_client.stepSimulation() + self.ReceiveObservation() + if default_motor_angles is not None: + num_steps_to_reset = int(reset_time / self.time_step) + for _ in range(num_steps_to_reset): + self.ApplyAction( + default_motor_angles, + motor_control_mode=robot_config.MotorControlMode.POSITION) + self._pybullet_client.stepSimulation() + self.ReceiveObservation() + + def GetURDFFile(self): + return os.path.join(self._urdf_root, "mini_cheetah/mini_cheetah.urdf") + + def ResetPose(self, add_constraint): + del add_constraint + for name in self._joint_name_to_id: + joint_id = self._joint_name_to_id[name] + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(joint_id), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=0) + for name, i in zip(MOTOR_NAMES, range(len(MOTOR_NAMES))): + angle = INIT_MOTOR_ANGLES[i] + self._pybullet_client.resetJointState( + self.quadruped, self._joint_name_to_id[name], angle, targetVelocity=0) + + def _BuildUrdfIds(self): + pass + + def _GetMotorNames(self): + return MOTOR_NAMES + + def _GetDefaultInitPosition(self): + if self._on_rack: + return INIT_RACK_POSITION + else: + return INIT_POSITION + + def _GetDefaultInitOrientation(self): + init_orientation = [0, 0, 0, 1.0] + return init_orientation diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah_test.py new file mode 100644 index 000000000..94acf54c0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/mini_cheetah_test.py @@ -0,0 +1,58 @@ +"""Tests for pybullet_envs.minitaur.robots.mini_cheetah. + +blaze test -c opt +//robotics/reinforcement_learning/minitaur/robots:mini_cheetah_test +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import numpy as np +from pybullet_envs.minitaur.envs import bullet_client +from pybullet_envs.minitaur.robots import mini_cheetah +from google3.testing.pybase import googletest + +PI = math.pi +NUM_STEPS = 500 +TIME_STEP = 0.002 +INIT_MOTOR_ANGLES = [0, -0.6, 1.4] * 4 + + +class MiniCheetahTest(googletest.TestCase): + + def test_init(self): + pybullet_client = bullet_client.BulletClient() + pybullet_client.enable_cns() + robot = mini_cheetah.MiniCheetah( + pybullet_client=pybullet_client, time_step=TIME_STEP, on_rack=True) + self.assertIsNotNone(robot) + + def test_static_pose_on_rack(self): + pybullet_client = bullet_client.BulletClient() + pybullet_client.enable_cns() + pybullet_client.resetSimulation() + pybullet_client.setPhysicsEngineParameter(numSolverIterations=60) + pybullet_client.setTimeStep(TIME_STEP) + pybullet_client.setGravity(0, 0, -10) + + robot = ( + mini_cheetah.MiniCheetah( + pybullet_client=pybullet_client, + action_repeat=5, + time_step=0.002, + on_rack=True)) + robot.Reset( + reload_urdf=False, + default_motor_angles=INIT_MOTOR_ANGLES, + reset_time=0.5) + for _ in range(NUM_STEPS): + robot.Step(INIT_MOTOR_ANGLES) + motor_angles = robot.GetMotorAngles() + np.testing.assert_array_almost_equal( + motor_angles, INIT_MOTOR_ANGLES, decimal=2) + + +if __name__ == '__main__': + googletest.main() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur.py new file mode 100644 index 000000000..9494c9ef0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur.py @@ -0,0 +1,1479 @@ +"""This file implements the functionalities of a minitaur using pybullet.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import copy +import logging +import math +import re +import numpy as np +import gin +from pybullet_envs.minitaur.robots import minitaur_constants +from pybullet_envs.minitaur.robots import minitaur_motor +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots.safety import safety_checker +from pybullet_envs.minitaur.robots.safety import safety_error +from pybullet_envs.minitaur.robots.utilities import action_filter +from pybullet_envs.minitaur.robots.utilities import kinematics + +INIT_POSITION = [0, 0, .2] +INIT_RACK_POSITION = [0, 0, 1] +INIT_ORIENTATION = [0, 0, 0, 1] +KNEE_CONSTRAINT_POINT_RIGHT = [0, 0.005, 0.2] +KNEE_CONSTRAINT_POINT_LEFT = [0, 0.01, 0.2] +OVERHEAT_SHUTDOWN_TORQUE = 2.45 +OVERHEAT_SHUTDOWN_TIME = 1.0 +LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"] +MOTOR_NAMES = [ + "motor_front_leftL_joint", "motor_front_leftR_joint", + "motor_back_leftL_joint", "motor_back_leftR_joint", + "motor_front_rightL_joint", "motor_front_rightR_joint", + "motor_back_rightL_joint", "motor_back_rightR_joint" +] +_CHASSIS_NAME_PATTERN = re.compile(r"chassis\D*center") +_MOTOR_NAME_PATTERN = re.compile(r"motor\D*joint") +_KNEE_NAME_PATTERN = re.compile(r"knee\D*") +_BRACKET_NAME_PATTERN = re.compile(r"motor\D*_bracket_joint") +_LEG_NAME_PATTERN1 = re.compile(r"hip\D*joint") +_LEG_NAME_PATTERN2 = re.compile(r"hip\D*link") +_LEG_NAME_PATTERN3 = re.compile(r"motor\D*link") +SENSOR_NOISE_STDDEV = (0.0, 0.0, 0.0, 0.0, 0.0) +MINITAUR_DEFAULT_MOTOR_DIRECTIONS = (-1, -1, -1, -1, 1, 1, 1, 1) +MINITAUR_DEFAULT_MOTOR_OFFSETS = (0, 0, 0, 0, 0, 0, 0, 0) +MINITAUR_NUM_MOTORS = 8 +TWO_PI = 2 * math.pi +MINITAUR_DOFS_PER_LEG = 2 + +URDF_ROOT = "robotics/reinforcement_learning/minitaur/robots/data/urdf/" + + +def MapToMinusPiToPi(angles): + """Maps a list of angles to [-pi, pi]. + + Args: + angles: A list of angles in rad. + + Returns: + A list of angle mapped to [-pi, pi]. + """ + mapped_angles = copy.deepcopy(angles) + for i in range(len(angles)): + mapped_angles[i] = math.fmod(angles[i], TWO_PI) + if mapped_angles[i] >= math.pi: + mapped_angles[i] -= TWO_PI + elif mapped_angles[i] < -math.pi: + mapped_angles[i] += TWO_PI + return mapped_angles + + +@gin.configurable +class Minitaur(object): + """The minitaur class that simulates a quadruped robot from Ghost Robotics.""" + + def __init__(self, + pybullet_client, + num_motors=MINITAUR_NUM_MOTORS, + dofs_per_leg=MINITAUR_DOFS_PER_LEG, + urdf_root=URDF_ROOT, + time_step=0.01, + action_repeat=1, + self_collision_enabled=False, + motor_control_mode=robot_config.MotorControlMode.POSITION, + motor_model_class=minitaur_motor.MotorModel, + motor_kp=1.0, + motor_kd=0.02, + motor_torque_limits=None, + pd_latency=0.0, + control_latency=0.0, + observation_noise_stdev=SENSOR_NOISE_STDDEV, + motor_overheat_protection=False, + motor_direction=MINITAUR_DEFAULT_MOTOR_DIRECTIONS, + motor_offset=MINITAUR_DEFAULT_MOTOR_OFFSETS, + on_rack=False, + reset_at_current_position=False, + sensors=None, + safety_config=None, + enable_action_interpolation=False, + enable_action_filter=False): + """Constructs a minitaur and reset it to the initial states. + + Args: + pybullet_client: The instance of BulletClient to manage different + simulations. + num_motors: The number of the motors on the robot. + dofs_per_leg: The number of degrees of freedom for each leg. + urdf_root: The path to the urdf folder. + time_step: The time step of the simulation. + action_repeat: The number of ApplyAction() for each control step. + self_collision_enabled: Whether to enable self collision. + motor_control_mode: Enum. Can either be POSITION, TORQUE, or HYBRID. + motor_model_class: We can choose from simple pd model to more accureate DC + motor models. + motor_kp: proportional gain for the motors. + motor_kd: derivative gain for the motors. + motor_torque_limits: Torque limits for the motors. Can be a single float + or a list of floats specifying different limits for different robots. If + not provided, the default limit of the robot is used. + pd_latency: The latency of the observations (in seconds) used to calculate + PD control. On the real hardware, it is the latency between the + microcontroller and the motor controller. + control_latency: The latency of the observations (in second) used to + calculate action. On the real hardware, it is the latency from the motor + controller, the microcontroller to the host (Nvidia TX2). + observation_noise_stdev: The standard deviation of a Gaussian noise model + for the sensor. It should be an array for separate sensors in the + following order [motor_angle, motor_velocity, motor_torque, + base_roll_pitch_yaw, base_angular_velocity] + motor_overheat_protection: Whether to shutdown the motor that has exerted + large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time + (OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more + details. + motor_direction: A list of direction values, either 1 or -1, to compensate + the axis difference of motors between the simulation and the real robot. + motor_offset: A list of offset value for the motor angles. This is used to + compensate the angle difference between the simulation and the real + robot. + on_rack: Whether to place the minitaur on rack. This is only used to debug + the walking gait. In this mode, the minitaur's base is hanged midair so + that its walking gait is clearer to visualize. + reset_at_current_position: Whether to reset the minitaur at the current + position and orientation. This is for simulating the reset behavior in + the real world. + sensors: a list of sensors that are attached to the robot. + safety_config: A SafetyConfig class to configure the safety layer. If + None, the safety layer will be disabled. + enable_action_interpolation: Whether to interpolate the current action + with the previous action in order to produce smoother motions + enable_action_filter: Boolean specifying if a lowpass filter should be + used to smooth actions. + """ + self.num_motors = num_motors + self.num_legs = self.num_motors // dofs_per_leg + self._pybullet_client = pybullet_client + self._action_repeat = action_repeat + self._urdf_root = urdf_root + self._self_collision_enabled = self_collision_enabled + self._motor_direction = motor_direction + self._motor_offset = motor_offset + self._observed_motor_torques = np.zeros(self.num_motors) + self._applied_motor_torques = np.zeros(self.num_motors) + self._max_force = 3.5 + self._pd_latency = pd_latency + self._control_latency = control_latency + self._observation_noise_stdev = observation_noise_stdev + self._observation_history = collections.deque(maxlen=100) + self._control_observation = [] + self._chassis_link_ids = [-1] + self._leg_link_ids = [] + self._motor_link_ids = [] + self._foot_link_ids = [] + self._motor_overheat_protection = motor_overheat_protection + self._on_rack = on_rack + self._reset_at_current_position = reset_at_current_position + self.SetAllSensors(sensors if sensors is not None else list()) + self.safety_config = safety_config + self._is_safe = True + self._safety_checker = None + + self._enable_action_interpolation = enable_action_interpolation + self._enable_action_filter = enable_action_filter + self._last_action = None + + if not motor_model_class: + raise ValueError("Must provide a motor model class!") + + if self._on_rack and self._reset_at_current_position: + raise ValueError("on_rack and reset_at_current_position " + "cannot be enabled together") + + if isinstance(motor_kp, (collections.Sequence, np.ndarray)): + self._motor_kps = np.asarray(motor_kp) + else: + self._motor_kps = np.full(num_motors, motor_kp) + + if isinstance(motor_kd, (collections.Sequence, np.ndarray)): + self._motor_kds = np.asarray(motor_kd) + else: + self._motor_kds = np.full(num_motors, motor_kd) + + if isinstance(motor_torque_limits, (collections.Sequence, np.ndarray)): + self._motor_torque_limits = np.asarray(motor_torque_limits) + elif motor_torque_limits is None: + self._motor_torque_limits = None + else: + self._motor_torque_limits = motor_torque_limits + + self._motor_control_mode = motor_control_mode + self._motor_model = motor_model_class( + kp=motor_kp, + kd=motor_kd, + torque_limits=self._motor_torque_limits, + motor_control_mode=motor_control_mode) + + self.time_step = time_step + self._step_counter = 0 + + # This also includes the time spent during the Reset motion. + self._state_action_counter = 0 + _, self._init_orientation_inv = self._pybullet_client.invertTransform( + position=[0, 0, 0], orientation=self._GetDefaultInitOrientation()) + + if self._enable_action_filter: + self._action_filter = self._BuildActionFilter() + # reset_time=-1.0 means skipping the reset motion. + # See Reset for more details. + self.Reset(reset_time=-1.0) + self.ReceiveObservation() + + return + + def GetTimeSinceReset(self): + return self._step_counter * self.time_step + + def _StepInternal(self, action, motor_control_mode=None): + self.ApplyAction(action, motor_control_mode) + self._pybullet_client.stepSimulation() + self.ReceiveObservation() + self._state_action_counter += 1 + + return + + def Step(self, action): + """Steps simulation.""" + if self._enable_action_filter: + action = self._FilterAction(action) + + for i in range(self._action_repeat): + proc_action = self.ProcessAction(action, i) + self._StepInternal(proc_action) + self._step_counter += 1 + + self._last_action = action + return + + def Terminate(self): + pass + + def GetKneeLinkIDs(self): + """Get list of IDs for all knee links.""" + return self._knee_link_ids + + def GetFootLinkIDs(self): + """Get list of IDs for all foot links.""" + return self._foot_link_ids + + def _RecordMassInfoFromURDF(self): + """Records the mass information from the URDF file.""" + self._base_mass_urdf = [] + for chassis_id in self._chassis_link_ids: + self._base_mass_urdf.append( + self._pybullet_client.getDynamicsInfo(self.quadruped, chassis_id)[0]) + self._leg_masses_urdf = [] + for leg_id in self._leg_link_ids: + self._leg_masses_urdf.append( + self._pybullet_client.getDynamicsInfo(self.quadruped, leg_id)[0]) + for motor_id in self._motor_link_ids: + self._leg_masses_urdf.append( + self._pybullet_client.getDynamicsInfo(self.quadruped, motor_id)[0]) + + def _RecordInertiaInfoFromURDF(self): + """Record the inertia of each body from URDF file.""" + self._link_urdf = [] + num_bodies = self._pybullet_client.getNumJoints(self.quadruped) + for body_id in range(-1, num_bodies): # -1 is for the base link. + inertia = self._pybullet_client.getDynamicsInfo(self.quadruped, + body_id)[2] + self._link_urdf.append(inertia) + # We need to use id+1 to index self._link_urdf because it has the base + # (index = -1) at the first element. + self._base_inertia_urdf = [ + self._link_urdf[chassis_id + 1] for chassis_id in self._chassis_link_ids + ] + self._leg_inertia_urdf = [ + self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids + ] + self._leg_inertia_urdf.extend( + [self._link_urdf[motor_id + 1] for motor_id in self._motor_link_ids]) + + def _BuildJointNameToIdDict(self): + num_joints = self._pybullet_client.getNumJoints(self.quadruped) + self._joint_name_to_id = {} + for i in range(num_joints): + joint_info = self._pybullet_client.getJointInfo(self.quadruped, i) + self._joint_name_to_id[joint_info[1].decode("UTF-8")] = joint_info[0] + + def _BuildUrdfIds(self): + """Build the link Ids from its name in the URDF file. + + Raises: + ValueError: Unknown category of the joint name. + """ + num_joints = self._pybullet_client.getNumJoints(self.quadruped) + self._chassis_link_ids = [-1] + # The self._leg_link_ids include both the upper and lower links of the leg. + self._leg_link_ids = [] + self._motor_link_ids = [] + self._foot_link_ids = [] + self._bracket_link_ids = [] + for i in range(num_joints): + joint_info = self._pybullet_client.getJointInfo(self.quadruped, i) + joint_name = joint_info[1].decode("UTF-8") + joint_id = self._joint_name_to_id[joint_name] + if _CHASSIS_NAME_PATTERN.match(joint_name): + self._chassis_link_ids.append(joint_id) + elif _BRACKET_NAME_PATTERN.match(joint_name): + self._bracket_link_ids.append(joint_id) + elif _MOTOR_NAME_PATTERN.match(joint_name): + self._motor_link_ids.append(joint_id) + elif _KNEE_NAME_PATTERN.match(joint_name): + self._foot_link_ids.append(joint_id) + elif (_LEG_NAME_PATTERN1.match(joint_name) or + _LEG_NAME_PATTERN2.match(joint_name) or + _LEG_NAME_PATTERN3.match(joint_name)): + self._leg_link_ids.append(joint_id) + else: + raise ValueError("Unknown category of joint %s" % joint_name) + + self._leg_link_ids.extend(self._foot_link_ids) + self._chassis_link_ids.sort() + self._motor_link_ids.sort() + self._foot_link_ids.sort() + self._leg_link_ids.sort() + self._bracket_link_ids.sort() + + def _RemoveDefaultJointDamping(self): + num_joints = self._pybullet_client.getNumJoints(self.quadruped) + for i in range(num_joints): + joint_info = self._pybullet_client.getJointInfo(self.quadruped, i) + self._pybullet_client.changeDynamics( + joint_info[0], -1, linearDamping=0, angularDamping=0) + + def _BuildMotorIdList(self): + self._motor_id_list = [ + self._joint_name_to_id[motor_name] + for motor_name in self._GetMotorNames() + ] + + def _CreateRackConstraint(self, init_position, init_orientation): + """Create a constraint that keeps the chassis at a fixed frame. + + This frame is defined by init_position and init_orientation. + + Args: + init_position: initial position of the fixed frame. + init_orientation: initial orientation of the fixed frame in quaternion + format [x,y,z,w]. + + Returns: + Return the constraint id. + """ + fixed_constraint = self._pybullet_client.createConstraint( + parentBodyUniqueId=self.quadruped, + parentLinkIndex=-1, + childBodyUniqueId=-1, + childLinkIndex=-1, + jointType=self._pybullet_client.JOINT_FIXED, + jointAxis=[0, 0, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=init_position, + childFrameOrientation=init_orientation) + return fixed_constraint + + def IsObservationValid(self): + """Whether the observation is valid for the current time step. + + In simulation, observations are always valid. In real hardware, it may not + be valid from time to time when communication error happens between the + Nvidia TX2 and the microcontroller. + + Returns: + Whether the observation is valid for the current time step. + """ + return True + + def Reset(self, reload_urdf=True, default_motor_angles=None, reset_time=3.0): + """Reset the minitaur to its initial states. + + Args: + reload_urdf: Whether to reload the urdf file. If not, Reset() just place + the minitaur back to its starting position. + default_motor_angles: The default motor angles. If it is None, minitaur + will hold a default pose (motor angle math.pi / 2) for 100 steps. In + torque control mode, the phase of holding the default pose is skipped. + reset_time: The duration (in seconds) to hold the default motor angles. If + reset_time <= 0 or in torque control mode, the phase of holding the + default pose is skipped. + """ + if reload_urdf: + self._LoadRobotURDF() + if self._on_rack: + self.rack_constraint = ( + self._CreateRackConstraint(self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation())) + self._BuildJointNameToIdDict() + self._BuildUrdfIds() + self._RemoveDefaultJointDamping() + self._BuildMotorIdList() + self._RecordMassInfoFromURDF() + self._RecordInertiaInfoFromURDF() + self.ResetPose(add_constraint=True) + else: + self._pybullet_client.resetBasePositionAndOrientation( + self.quadruped, self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation()) + self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], + [0, 0, 0]) + self.ResetPose(add_constraint=False) + + self._overheat_counter = np.zeros(self.num_motors) + self._motor_enabled_list = [True] * self.num_motors + self._observation_history.clear() + self._step_counter = 0 + self._state_action_counter = 0 + self._is_safe = True + self._last_action = None + + # Enable the safety layer before we perform any reset motions. + if self.safety_config: + self._safety_checker = safety_checker.SafetyChecker(self) + self._SettleDownForReset(default_motor_angles, reset_time) + + if self._enable_action_filter: + self._ResetActionFilter() + + return + + def _LoadRobotURDF(self): + """Loads the URDF file for the robot.""" + urdf_file = self.GetURDFFile() + if self._self_collision_enabled: + self.quadruped = self._pybullet_client.loadURDF( + urdf_file, + self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation(), + flags=self._pybullet_client.URDF_USE_SELF_COLLISION) + else: + self.quadruped = self._pybullet_client.loadURDF( + urdf_file, self._GetDefaultInitPosition(), + self._GetDefaultInitOrientation()) + + def _SettleDownForReset(self, default_motor_angles, reset_time): + """Sets the default motor angles and waits for the robot to settle down. + + The reset is skipped is reset_time is less than zereo. + + Args: + default_motor_angles: A list of motor angles that the robot will achieve + at the end of the reset phase. + reset_time: The time duration for the reset phase. + """ + if reset_time <= 0: + return + + # Important to fill the observation buffer. + self.ReceiveObservation() + for _ in range(100): + self._StepInternal( + [math.pi / 2] * self.num_motors, + motor_control_mode=robot_config.MotorControlMode.POSITION) + # Don't continue to reset if a safety error has occurred. + if not self._is_safe: + return + + if default_motor_angles is None: + return + + num_steps_to_reset = int(reset_time / self.time_step) + for _ in range(num_steps_to_reset): + self._StepInternal( + default_motor_angles, + motor_control_mode=robot_config.MotorControlMode.POSITION) + # Don't continue to reset if a safety error has occurred. + if not self._is_safe: + return + + def _SetMotorTorqueById(self, motor_id, torque): + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=motor_id, + controlMode=self._pybullet_client.TORQUE_CONTROL, + force=torque) + + def _SetMotorTorqueByIds(self, motor_ids, torques): + self._pybullet_client.setJointMotorControlArray( + bodyIndex=self.quadruped, + jointIndices=motor_ids, + controlMode=self._pybullet_client.TORQUE_CONTROL, + forces=torques) + + def _SetDesiredMotorAngleByName(self, motor_name, desired_angle): + self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], + desired_angle) + + def GetURDFFile(self): + return "%s/quadruped/minitaur.urdf" % self._urdf_root + + def ResetPose(self, add_constraint): + """Reset the pose of the minitaur. + + Args: + add_constraint: Whether to add a constraint at the joints of two feet. + """ + for i in range(self.num_legs): + self._ResetPoseForLeg(i, add_constraint) + + def _ResetPoseForLeg(self, leg_id, add_constraint): + """Reset the initial pose for the leg. + + Args: + leg_id: It should be 0, 1, 2, or 3, which represents the leg at + front_left, back_left, front_right and back_right. + add_constraint: Whether to add a constraint at the joints of two feet. + """ + knee_friction_force = 0 + half_pi = math.pi / 2.0 + knee_angle = -2.1834 + + leg_position = LEG_POSITION[leg_id] + self._pybullet_client.resetJointState( + self.quadruped, + self._joint_name_to_id["motor_" + leg_position + "L_joint"], + self._motor_direction[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState( + self.quadruped, + self._joint_name_to_id["knee_" + leg_position + "L_link"], + self._motor_direction[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState( + self.quadruped, + self._joint_name_to_id["motor_" + leg_position + "R_joint"], + self._motor_direction[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState( + self.quadruped, + self._joint_name_to_id["knee_" + leg_position + "R_link"], + self._motor_direction[2 * leg_id + 1] * knee_angle, + targetVelocity=0) + if add_constraint: + self._pybullet_client.createConstraint( + self.quadruped, + self._joint_name_to_id["knee_" + leg_position + "R_link"], + self.quadruped, + self._joint_name_to_id["knee_" + leg_position + "L_link"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], + KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT) + + # Disable the default motor in pybullet. + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(self._joint_name_to_id["motor_" + leg_position + + "L_joint"]), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=knee_friction_force) + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(self._joint_name_to_id["motor_" + leg_position + + "R_joint"]), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=knee_friction_force) + + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(self._joint_name_to_id["knee_" + leg_position + "L_link"]), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=knee_friction_force) + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=(self._joint_name_to_id["knee_" + leg_position + "R_link"]), + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=knee_friction_force) + + def GetBasePosition(self): + """Get the position of minitaur's base. + + Returns: + The position of minitaur's base. + """ + return self._base_position + + def GetBaseVelocity(self): + """Get the linear velocity of minitaur's base. + + Returns: + The velocity of minitaur's base. + """ + velocity, _ = self._pybullet_client.getBaseVelocity(self.quadruped) + return velocity + + def GetTrueBaseRollPitchYaw(self): + """Get minitaur's base orientation in euler angle in the world frame. + + Returns: + A tuple (roll, pitch, yaw) of the base in world frame. + """ + orientation = self.GetTrueBaseOrientation() + roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion(orientation) + return np.asarray(roll_pitch_yaw) + + def GetBaseRollPitchYaw(self): + """Get minitaur's base orientation in euler angle in the world frame. + + This function mimicks the noisy sensor reading and adds latency. + Returns: + A tuple (roll, pitch, yaw) of the base in world frame polluted by noise + and latency. + """ + delayed_orientation = np.array( + self._control_observation[3 * self.num_motors:3 * self.num_motors + 4]) + delayed_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion( + delayed_orientation) + roll_pitch_yaw = self._AddSensorNoise( + np.array(delayed_roll_pitch_yaw), self._observation_noise_stdev[3]) + return roll_pitch_yaw + + def GetHipPositionsInBaseFrame(self): + """Get the hip joint positions of the robot within its base frame.""" + raise NotImplementedError("Not implemented for Minitaur.") + + def _EndEffectorIK(self, leg_id, position, position_in_world_frame): + """Calculate the joint positions from the end effector position.""" + assert len(self._foot_link_ids) == self.num_legs + toe_id = self._foot_link_ids[leg_id] + motors_per_leg = self.num_motors // self.num_legs + joint_position_idxs = [ + i for i in range(leg_id * motors_per_leg, leg_id * motors_per_leg + + motors_per_leg) + ] + joint_angles = kinematics.joint_angles_from_link_position( + robot=self, + link_position=position, + link_id=toe_id, + joint_ids=joint_position_idxs, + position_in_world_frame=position_in_world_frame) + # Joint offset is necessary for Laikago. + joint_angles = np.multiply( + np.asarray(joint_angles) - + np.asarray(self._motor_offset)[joint_position_idxs], + self._motor_direction[joint_position_idxs]) + # Return the joing index (the same as when calling GetMotorAngles) as well + # as the angles. + return joint_position_idxs, joint_angles.tolist() + + # TODO(b/154361633): Implements an array version of this following function. + def ComputeMotorAnglesFromFootWorldPosition(self, leg_id, + foot_world_position): + """Use IK to compute the motor angles, given the foot link's position. + + Args: + leg_id: The leg index. + foot_world_position: The foot link's position in the world frame. + + Returns: + A tuple. The position indices and the angles for all joints along the + leg. The position indices is consistent with the joint orders as returned + by GetMotorAngles API. + """ + return self._EndEffectorIK( + leg_id, foot_world_position, position_in_world_frame=True) + + def ComputeMotorAnglesFromFootLocalPosition(self, leg_id, + foot_local_position): + """Use IK to compute the motor angles, given the foot link's local position. + + Args: + leg_id: The leg index. + foot_local_position: The foot link's position in the base frame. + + Returns: + A tuple. The position indices and the angles for all joints along the + leg. The position indices is consistent with the joint orders as returned + by GetMotorAngles API. + """ + return self._EndEffectorIK( + leg_id, foot_local_position, position_in_world_frame=False) + + def ComputeJacobian(self, leg_id): + """Compute the Jacobian for a given leg.""" + # Does not work for Minitaur which has the four bar mechanism for now. + assert len(self._foot_link_ids) == self.num_legs + return kinematics.compute_jacobian( + robot=self, + link_id=self._foot_link_ids[leg_id], + ) + + def MapContactForceToJointTorques(self, leg_id, contact_force): + """Maps the foot contact force to the leg joint torques.""" + jv = self.ComputeJacobian(leg_id) + all_motor_torques = np.matmul(contact_force, jv) + motor_torques = {} + motors_per_leg = self.num_motors // self.num_legs + com_dof = 6 + for joint_id in range(leg_id * motors_per_leg, + (leg_id + 1) * motors_per_leg): + motor_torques[joint_id] = all_motor_torques[ + com_dof + joint_id] * self._motor_direction[joint_id] + + return motor_torques + + def GetFootContacts(self): + """Get minitaur's foot contact situation with the ground. + + Returns: + A list of 4 booleans. The ith boolean is True if leg i is in contact with + ground. + """ + contacts = [] + for leg_idx in range(MINITAUR_NUM_MOTORS // 2): + link_id_1 = self._foot_link_ids[leg_idx * 2] + link_id_2 = self._foot_link_ids[leg_idx * 2 + 1] + contact_1 = bool( + self._pybullet_client.getContactPoints( + bodyA=0, + bodyB=self.quadruped, + linkIndexA=-1, + linkIndexB=link_id_1)) + contact_2 = bool( + self._pybullet_client.getContactPoints( + bodyA=0, + bodyB=self.quadruped, + linkIndexA=-1, + linkIndexB=link_id_2)) + contacts.append(contact_1 or contact_2) + return contacts + + def GetFootPositionsInWorldFrame(self): + """Get the robot's foot position in the base frame.""" + assert len(self._foot_link_ids) == self.num_legs + foot_positions = [] + for foot_id in self.GetFootLinkIDs(): + foot_positions.append( + kinematics.link_position_in_world_frame( + robot=self, + link_id=foot_id, + )) + return np.array(foot_positions) + + def GetFootPositionsInBaseFrame(self): + """Get the robot's foot position in the base frame.""" + assert len(self._foot_link_ids) == self.num_legs + foot_positions = [] + for foot_id in self.GetFootLinkIDs(): + foot_positions.append( + kinematics.link_position_in_base_frame( + robot=self, + link_id=foot_id, + )) + return np.array(foot_positions) + + def GetTrueMotorAngles(self): + """Gets the eight motor angles at the current moment, mapped to [-pi, pi]. + + Returns: + Motor angles, mapped to [-pi, pi]. + """ + motor_angles = [state[0] for state in self._joint_states] + motor_angles = np.multiply( + np.asarray(motor_angles) - np.asarray(self._motor_offset), + self._motor_direction) + return motor_angles + + def GetMotorAngles(self): + """Gets the eight motor angles. + + This function mimicks the noisy sensor reading and adds latency. The motor + angles that are delayed, noise polluted, and mapped to [-pi, pi]. + + Returns: + Motor angles polluted by noise and latency, mapped to [-pi, pi]. + """ + motor_angles = self._AddSensorNoise( + np.array(self._control_observation[0:self.num_motors]), + self._observation_noise_stdev[0]) + return MapToMinusPiToPi(motor_angles) + + def GetTrueMotorVelocities(self): + """Get the velocity of all eight motors. + + Returns: + Velocities of all eight motors. + """ + motor_velocities = [state[1] for state in self._joint_states] + + motor_velocities = np.multiply(motor_velocities, self._motor_direction) + return motor_velocities + + def GetMotorVelocities(self): + """Get the velocity of all eight motors. + + This function mimicks the noisy sensor reading and adds latency. + Returns: + Velocities of all eight motors polluted by noise and latency. + """ + return self._AddSensorNoise( + np.array(self._control_observation[self.num_motors:2 * + self.num_motors]), + self._observation_noise_stdev[1]) + + def GetTrueMotorTorques(self): + """Get the amount of torque the motors are exerting. + + Returns: + Motor torques of all eight motors. + """ + return self._observed_motor_torques + + def GetMotorTorques(self): + """Get the amount of torque the motors are exerting. + + This function mimicks the noisy sensor reading and adds latency. + Returns: + Motor torques of all eight motors polluted by noise and latency. + """ + return self._AddSensorNoise( + np.array(self._control_observation[2 * self.num_motors:3 * + self.num_motors]), + self._observation_noise_stdev[2]) + + def GetEnergyConsumptionPerControlStep(self): + """Get the amount of energy used in last one time step. + + Returns: + Energy Consumption based on motor velocities and torques (Nm^2/s). + """ + return np.abs(np.dot( + self.GetMotorTorques(), + self.GetMotorVelocities())) * self.time_step * self._action_repeat + + def GetTrueBaseOrientation(self): + """Get the orientation of minitaur's base, represented as quaternion. + + Returns: + The orientation of minitaur's base. + """ + return self._base_orientation + + def GetBaseOrientation(self): + """Get the orientation of minitaur's base, represented as quaternion. + + This function mimicks the noisy sensor reading and adds latency. + Returns: + The orientation of minitaur's base polluted by noise and latency. + """ + return self._pybullet_client.getQuaternionFromEuler( + self.GetBaseRollPitchYaw()) + + def GetTrueBaseRollPitchYawRate(self): + """Get the rate of orientation change of the minitaur's base in euler angle. + + Returns: + rate of (roll, pitch, yaw) change of the minitaur's base. + """ + angular_velocity = self._pybullet_client.getBaseVelocity(self.quadruped)[1] + orientation = self.GetTrueBaseOrientation() + return self.TransformAngularVelocityToLocalFrame(angular_velocity, + orientation) + + def TransformAngularVelocityToLocalFrame(self, angular_velocity, orientation): + """Transform the angular velocity from world frame to robot's frame. + + Args: + angular_velocity: Angular velocity of the robot in world frame. + orientation: Orientation of the robot represented as a quaternion. + + Returns: + angular velocity of based on the given orientation. + """ + # Treat angular velocity as a position vector, then transform based on the + # orientation given by dividing (or multiplying with inverse). + # Get inverse quaternion assuming the vector is at 0,0,0 origin. + _, orientation_inversed = self._pybullet_client.invertTransform([0, 0, 0], + orientation) + # Transform the angular_velocity at neutral orientation using a neutral + # translation and reverse of the given orientation. + relative_velocity, _ = self._pybullet_client.multiplyTransforms( + [0, 0, 0], orientation_inversed, angular_velocity, + self._pybullet_client.getQuaternionFromEuler([0, 0, 0])) + return np.asarray(relative_velocity) + + def GetBaseRollPitchYawRate(self): + """Get the rate of orientation change of the minitaur's base in euler angle. + + This function mimicks the noisy sensor reading and adds latency. + Returns: + rate of (roll, pitch, yaw) change of the minitaur's base polluted by noise + and latency. + """ + return self._AddSensorNoise( + np.array(self._control_observation[3 * self.num_motors + + 4:3 * self.num_motors + 7]), + self._observation_noise_stdev[4]) + + def GetActionDimension(self): + """Get the length of the action list. + + Returns: + The length of the action list. + """ + return self.num_motors + + def _ApplyOverheatProtection(self, actual_torque): + if self._motor_overheat_protection: + for i in range(self.num_motors): + if abs(actual_torque[i]) > OVERHEAT_SHUTDOWN_TORQUE: + self._overheat_counter[i] += 1 + else: + self._overheat_counter[i] = 0 + if (self._overheat_counter[i] > + OVERHEAT_SHUTDOWN_TIME / self.time_step): + self._motor_enabled_list[i] = False + + def ApplyAction(self, motor_commands, motor_control_mode=None): + """Apply the motor commands using the motor model. + + Args: + motor_commands: np.array. Can be motor angles, torques, hybrid commands, + or motor pwms (for Minitaur only). + motor_control_mode: A MotorControlMode enum. + """ + self.last_action_time = self._state_action_counter * self.time_step + control_mode = motor_control_mode + if control_mode is None: + control_mode = self._motor_control_mode + if self._safety_checker: + try: + self._safety_checker.check_motor_action(motor_commands, control_mode) + except safety_error.SafetyError as e: + logging.info("A safety error occurred: %s", e) + self._is_safe = False + return + motor_commands = np.asarray(motor_commands) + + q, qdot = self._GetPDObservation() + qdot_true = self.GetTrueMotorVelocities() + actual_torque, observed_torque = self._motor_model.convert_to_torque( + motor_commands, q, qdot, qdot_true, control_mode) + + # May turn off the motor + self._ApplyOverheatProtection(actual_torque) + + # The torque is already in the observation space because we use + # GetMotorAngles and GetMotorVelocities. + self._observed_motor_torques = observed_torque + + # Transform into the motor space when applying the torque. + self._applied_motor_torque = np.multiply(actual_torque, + self._motor_direction) + motor_ids = [] + motor_torques = [] + + for motor_id, motor_torque, motor_enabled in zip(self._motor_id_list, + self._applied_motor_torque, + self._motor_enabled_list): + if motor_enabled: + motor_ids.append(motor_id) + motor_torques.append(motor_torque) + else: + motor_ids.append(motor_id) + motor_torques.append(0) + self._SetMotorTorqueByIds(motor_ids, motor_torques) + + def ConvertFromLegModel(self, actions): + """Convert the actions that use leg model to the real motor actions. + + Args: + actions: The theta, phi of the leg model. + + Returns: + The eight desired motor angles that can be used in ApplyActions(). + """ + motor_angle = copy.deepcopy(actions) + scale_for_singularity = 1 + offset_for_singularity = 1.5 + half_num_motors = self.num_motors // 2 + quater_pi = math.pi / 4 + for i in range(self.num_motors): + action_idx = i // 2 + forward_backward_component = ( + -scale_for_singularity * quater_pi * + (actions[action_idx + half_num_motors] + offset_for_singularity)) + extension_component = (-1)**i * quater_pi * actions[action_idx] + if i >= half_num_motors: + extension_component = -extension_component + motor_angle[i] = ( + math.pi + forward_backward_component + extension_component) + return motor_angle + + def GetBaseMassesFromURDF(self): + """Get the mass of the base from the URDF file.""" + return self._base_mass_urdf + + def GetBaseInertiasFromURDF(self): + """Get the inertia of the base from the URDF file.""" + return self._base_inertia_urdf + + def GetLegMassesFromURDF(self): + """Get the mass of the legs from the URDF file.""" + return self._leg_masses_urdf + + def GetLegInertiasFromURDF(self): + """Get the inertia of the legs from the URDF file.""" + return self._leg_inertia_urdf + + def SetBaseMasses(self, base_mass): + """Set the mass of minitaur's base. + + Args: + base_mass: A list of masses of each body link in CHASIS_LINK_IDS. The + length of this list should be the same as the length of CHASIS_LINK_IDS. + + Raises: + ValueError: It is raised when the length of base_mass is not the same as + the length of self._chassis_link_ids. + """ + if len(base_mass) != len(self._chassis_link_ids): + raise ValueError( + "The length of base_mass {} and self._chassis_link_ids {} are not " + "the same.".format(len(base_mass), len(self._chassis_link_ids))) + for chassis_id, chassis_mass in zip(self._chassis_link_ids, base_mass): + self._pybullet_client.changeDynamics( + self.quadruped, chassis_id, mass=chassis_mass) + + def SetLegMasses(self, leg_masses): + """Set the mass of the legs. + + A leg includes leg_link and motor. 4 legs contain 16 links (4 links each) + and 8 motors. First 16 numbers correspond to link masses, last 8 correspond + to motor masses (24 total). + + Args: + leg_masses: The leg and motor masses for all the leg links and motors. + + Raises: + ValueError: It is raised when the length of masses is not equal to number + of links + motors. + """ + if len(leg_masses) != len(self._leg_link_ids) + len(self._motor_link_ids): + raise ValueError("The number of values passed to SetLegMasses are " + "different than number of leg links and motors.") + for leg_id, leg_mass in zip(self._leg_link_ids, leg_masses): + self._pybullet_client.changeDynamics( + self.quadruped, leg_id, mass=leg_mass) + motor_masses = leg_masses[len(self._leg_link_ids):] + for link_id, motor_mass in zip(self._motor_link_ids, motor_masses): + self._pybullet_client.changeDynamics( + self.quadruped, link_id, mass=motor_mass) + + def SetBaseInertias(self, base_inertias): + """Set the inertias of minitaur's base. + + Args: + base_inertias: A list of inertias of each body link in CHASIS_LINK_IDS. + The length of this list should be the same as the length of + CHASIS_LINK_IDS. + + Raises: + ValueError: It is raised when the length of base_inertias is not the same + as the length of self._chassis_link_ids and base_inertias contains + negative values. + """ + if len(base_inertias) != len(self._chassis_link_ids): + raise ValueError( + "The length of base_inertias {} and self._chassis_link_ids {} are " + "not the same.".format( + len(base_inertias), len(self._chassis_link_ids))) + for chassis_id, chassis_inertia in zip(self._chassis_link_ids, + base_inertias): + for inertia_value in chassis_inertia: + if (np.asarray(inertia_value) < 0).any(): + raise ValueError("Values in inertia matrix should be non-negative.") + self._pybullet_client.changeDynamics( + self.quadruped, chassis_id, localInertiaDiagonal=chassis_inertia) + + def SetLegInertias(self, leg_inertias): + """Set the inertias of the legs. + + A leg includes leg_link and motor. 4 legs contain 16 links (4 links each) + and 8 motors. First 16 numbers correspond to link inertia, last 8 correspond + to motor inertia (24 total). + + Args: + leg_inertias: The leg and motor inertias for all the leg links and motors. + + Raises: + ValueError: It is raised when the length of inertias is not equal to + the number of links + motors or leg_inertias contains negative values. + """ + + if len(leg_inertias) != len(self._leg_link_ids) + len(self._motor_link_ids): + raise ValueError("The number of values passed to SetLegMasses are " + "different than number of leg links and motors.") + for leg_id, leg_inertia in zip(self._leg_link_ids, leg_inertias): + for inertia_value in leg_inertias: + if (np.asarray(inertia_value) < 0).any(): + raise ValueError("Values in inertia matrix should be non-negative.") + self._pybullet_client.changeDynamics( + self.quadruped, leg_id, localInertiaDiagonal=leg_inertia) + + motor_inertias = leg_inertias[len(self._leg_link_ids):] + for link_id, motor_inertia in zip(self._motor_link_ids, motor_inertias): + for inertia_value in motor_inertias: + if (np.asarray(inertia_value) < 0).any(): + raise ValueError("Values in inertia matrix should be non-negative.") + self._pybullet_client.changeDynamics( + self.quadruped, link_id, localInertiaDiagonal=motor_inertia) + + def SetFootFriction(self, foot_friction): + """Set the lateral friction of the feet. + + Args: + foot_friction: The lateral friction coefficient of the foot. This value is + shared by all four feet. + """ + for link_id in self._foot_link_ids: + self._pybullet_client.changeDynamics( + self.quadruped, link_id, lateralFriction=foot_friction) + + # TODO(b/73748980): Add more API's to set other contact parameters. + def SetFootRestitution(self, foot_restitution): + """Set the coefficient of restitution at the feet. + + Args: + foot_restitution: The coefficient of restitution (bounciness) of the feet. + This value is shared by all four feet. + """ + for link_id in self._foot_link_ids: + self._pybullet_client.changeDynamics( + self.quadruped, link_id, restitution=foot_restitution) + + def SetJointFriction(self, joint_frictions): + for knee_joint_id, friction in zip(self._foot_link_ids, joint_frictions): + self._pybullet_client.setJointMotorControl2( + bodyIndex=self.quadruped, + jointIndex=knee_joint_id, + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=friction) + + def GetNumKneeJoints(self): + return len(self._foot_link_ids) + + def SetBatteryVoltage(self, voltage): + self._motor_model.set_voltage(voltage) + + def SetMotorViscousDamping(self, viscous_damping): + self._motor_model.set_viscous_damping(viscous_damping) + + def GetTrueObservation(self): + observation = [] + observation.extend(self.GetTrueMotorAngles()) + observation.extend(self.GetTrueMotorVelocities()) + observation.extend(self.GetTrueMotorTorques()) + observation.extend(self.GetTrueBaseOrientation()) + observation.extend(self.GetTrueBaseRollPitchYawRate()) + return observation + + def ReceiveObservation(self): + """Receive the observation from sensors. + + This function is called once per step. The observations are only updated + when this function is called. + """ + self._joint_states = self._pybullet_client.getJointStates( + self.quadruped, self._motor_id_list) + self._base_position, orientation = ( + self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) + # Computes the relative orientation relative to the robot's + # initial_orientation. + _, self._base_orientation = self._pybullet_client.multiplyTransforms( + positionA=[0, 0, 0], + orientationA=orientation, + positionB=[0, 0, 0], + orientationB=self._init_orientation_inv) + self._observation_history.appendleft(self.GetTrueObservation()) + self._control_observation = self._GetControlObservation() + self.last_state_time = self._state_action_counter * self.time_step + if self._safety_checker: + try: + self._safety_checker.check_state() + except safety_error.SafetyError as e: + logging.info("A safety error occurred: %s", e) + self._is_safe = False + + def _GetDelayedObservation(self, latency): + """Get observation that is delayed by the amount specified in latency. + + Args: + latency: The latency (in seconds) of the delayed observation. + + Returns: + observation: The observation which was actually latency seconds ago. + """ + if latency <= 0 or len(self._observation_history) == 1: + observation = self._observation_history[0] + else: + n_steps_ago = int(latency / self.time_step) + if n_steps_ago + 1 >= len(self._observation_history): + return self._observation_history[-1] + remaining_latency = latency - n_steps_ago * self.time_step + blend_alpha = remaining_latency / self.time_step + observation = ( + (1.0 - blend_alpha) * np.array(self._observation_history[n_steps_ago]) + + blend_alpha * np.array(self._observation_history[n_steps_ago + 1])) + return observation + + def _GetPDObservation(self): + pd_delayed_observation = self._GetDelayedObservation(self._pd_latency) + q = pd_delayed_observation[0:self.num_motors] + qdot = pd_delayed_observation[self.num_motors:2 * self.num_motors] + return (np.array(q), np.array(qdot)) + + def _GetControlObservation(self): + control_delayed_observation = self._GetDelayedObservation( + self._control_latency) + return control_delayed_observation + + def _AddSensorNoise(self, sensor_values, noise_stdev): + if noise_stdev <= 0: + return sensor_values + observation = sensor_values + np.random.normal( + scale=noise_stdev, size=sensor_values.shape) + return observation + + def SetControlLatency(self, latency): + """Set the latency of the control loop. + + It measures the duration between sending an action from Nvidia TX2 and + receiving the observation from microcontroller. + + Args: + latency: The latency (in seconds) of the control loop. + """ + self._control_latency = latency + + def GetControlLatency(self): + """Get the control latency. + + Returns: + The latency (in seconds) between when the motor command is sent and when + the sensor measurements are reported back to the controller. + """ + return self._control_latency + + # TODO(b/73666007): Change the API to SetMotorPGains and SetMotorDGains. + def SetMotorGains(self, kp, kd): + """Set the gains of all motors. + + These gains are PD gains for motor positional control. kp is the + proportional gain and kd is the derivative gain. + + Args: + kp: proportional gain(s) of the motors. + kd: derivative gain(s) of the motors. + """ + if isinstance(kp, (collections.Sequence, np.ndarray)): + self._motor_kps = np.asarray(kp) + else: + self._motor_kps = np.full(self.num_motors, kp) + + if isinstance(kd, (collections.Sequence, np.ndarray)): + self._motor_kds = np.asarray(kd) + else: + self._motor_kds = np.full(self.num_motors, kd) + + self._motor_model.set_motor_gains(kp, kd) + + # TODO(b/73666007): Change the API to GetMotorPGains and GetMotorDGains. + def GetMotorGains(self): + """Get the gains of the motor. + + Returns: + The proportional gain. + The derivative gain. + """ + return self._motor_kps, self._motor_kds + + def GetMotorPositionGains(self): + """Get the position gains of the motor. + + Returns: + The proportional gain. + """ + return self._motor_kps + + def GetMotorVelocityGains(self): + """Get the velocity gains of the motor. + + Returns: + The derivative gain. + """ + return self._motor_kds + + def SetMotorStrengthRatio(self, ratio): + """Set the strength of all motors relative to the default value. + + Args: + ratio: The relative strength. A scalar range from 0.0 to 1.0. + """ + self._motor_model.set_strength_ratios([ratio] * self.num_motors) + + def SetMotorStrengthRatios(self, ratios): + """Set the strength of each motor relative to the default value. + + Args: + ratios: The relative strength. A numpy array ranging from 0.0 to 1.0. + """ + self._motor_model.set_strength_ratios(ratios) + + def SetTimeSteps(self, action_repeat, simulation_step): + """Set the time steps of the control and simulation. + + Args: + action_repeat: The number of simulation steps that the same action is + repeated. + simulation_step: The simulation time step. + """ + self.time_step = simulation_step + self._action_repeat = action_repeat + + def _GetMotorNames(self): + return MOTOR_NAMES + + def _GetDefaultInitPosition(self): + """Returns the init position of the robot. + + It can be either 1) origin (INIT_POSITION), 2) origin with a rack + (INIT_RACK_POSITION), or 3) the previous position. + """ + # If we want continuous resetting and is not the first episode. + if self._reset_at_current_position and self._observation_history: + x, y, _ = self.GetBasePosition() + _, _, z = INIT_POSITION + return [x, y, z] + + if self._on_rack: + return INIT_RACK_POSITION + else: + return INIT_POSITION + + def _GetDefaultInitOrientation(self): + """Returns the init position of the robot. + + It can be either 1) INIT_ORIENTATION or 2) the previous rotation in yaw. + """ + # If we want continuous resetting and is not the first episode. + if self._reset_at_current_position and self._observation_history: + _, _, yaw = self.GetBaseRollPitchYaw() + return self._pybullet_client.getQuaternionFromEuler([0.0, 0.0, yaw]) + return INIT_ORIENTATION + + @property + def chassis_link_ids(self): + return self._chassis_link_ids + + def SetAllSensors(self, sensors): + """set all sensors to this robot and move the ownership to this robot. + + Args: + sensors: a list of sensors to this robot. + """ + for s in sensors: + s.set_robot(self) + self._sensors = sensors + + def GetAllSensors(self): + """get all sensors associated with this robot. + + Returns: + sensors: a list of all sensors. + """ + return self._sensors + + def GetSensor(self, name): + """get the first sensor with the given name. + + This function return None if a sensor with the given name does not exist. + + Args: + name: the name of the sensor we are looking + + Returns: + sensor: a sensor with the given name. None if not exists. + """ + for s in self._sensors: + if s.get_name() == name: + return s + return None + + @property + def is_safe(self): + return self._is_safe + + @property + def last_action(self): + return self._last_action + + def ProcessAction(self, action, substep_count): + """If enabled, interpolates between the current and previous actions. + + Args: + action: current action. + substep_count: the step count should be between [0, self.__action_repeat). + + Returns: + If interpolation is enabled, returns interpolated action depending on + the current action repeat substep. + """ + if self._enable_action_interpolation: + if self._last_action is not None: + prev_action = self._last_action + else: + prev_action = self.GetMotorAngles() + + lerp = float(substep_count + 1) / self._action_repeat + proc_action = prev_action + lerp * (action - prev_action) + else: + proc_action = action + + return proc_action + + def _BuildActionFilter(self): + sampling_rate = 1 / (self.time_step * self._action_repeat) + num_joints = self.GetActionDimension() + a_filter = action_filter.ActionFilterButter( + sampling_rate=sampling_rate, num_joints=num_joints) + return a_filter + + def _ResetActionFilter(self): + self._action_filter.reset() + return + + def _FilterAction(self, action): + # initialize the filter history, since resetting the filter will fill + # the history with zeros and this can cause sudden movements at the start + # of each episode + if self._step_counter == 0: + default_action = self.GetMotorAngles() + self._action_filter.init_history(default_action) + + filtered_action = self._action_filter.filter(action) + return filtered_action + + @property + def pybullet_client(self): + return self._pybullet_client + + @property + def joint_states(self): + return self._joint_states + + @classmethod + def GetConstants(cls): + del cls + return minitaur_constants diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_constants.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_constants.py new file mode 100644 index 000000000..9a9ed2e69 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_constants.py @@ -0,0 +1,62 @@ +# Lint as: python3 +"""Defines the minitaur robot related constants and URDF specs.""" + +import collections +import math + +import gin + +MINITAUR_URDF_PATH = "quadruped/minitaur_rainbow_dash.urdf" + +INIT_POSITION = (0, 0, 0.2) +INIT_RACK_POSITION = (0, 0, 1) +INIT_ORIENTATION_QUAT = (0, 0, 0, 1) +INIT_ORIENTATION_RPY = (0, 0, 0) + +NUM_LEGS = 4 + +JOINT_NAMES = ("motor_front_leftL_joint", "motor_front_leftR_joint", + "motor_back_leftL_joint", "motor_back_leftR_joint", + "motor_front_rightL_joint", "motor_front_rightR_joint", + "motor_back_rightL_joint", "motor_back_rightR_joint") + +INIT_JOINT_ANGLES = collections.OrderedDict( + zip(JOINT_NAMES, [math.pi / 2, math.pi / 2] * NUM_LEGS)) + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_DIRECTIONS = collections.OrderedDict( + zip(JOINT_NAMES, (-1, -1, -1, -1, 1, 1, 1, 1))) + +# Used to convert the robot SDK joint angles to URDF joint angles. +JOINT_OFFSETS = collections.OrderedDict( + zip(JOINT_NAMES, (0, 0, 0, 0, 0, 0, 0, 0))) + +LEG_ORDER = ["front_left", "back_left", "front_right", "back_right"] + +END_EFFECTOR_NAMES = ( + "knee_front_rightR_joint", + "knee_front_leftL_joint", + "knee_back_rightR_joint", + "knee_back_leftL_joint", +) + +MOTOR_NAMES = JOINT_NAMES +MOTOR_GROUP = collections.OrderedDict((("body_motors", JOINT_NAMES),)) + +KNEE_CONSTRAINT_POINT_LONG = [0, 0.0045, 0.088] +KNEE_CONSTRAINT_POINT_SHORT = [0, 0.0045, 0.100] + +# Add the gin constants to be used for gin binding in config. +gin.constant("minitaur_constants.MINITAUR_URDF_PATH", MINITAUR_URDF_PATH) +gin.constant("minitaur_constants.MINITAUR_INIT_POSITION", INIT_POSITION) +gin.constant("minitaur_constants.MINITAUR_INIT_ORIENTATION_QUAT", + INIT_ORIENTATION_QUAT) +gin.constant("minitaur_constants.MINITAUR_INIT_ORIENTATION_RPY", + INIT_ORIENTATION_RPY) +gin.constant("minitaur_constants.MINITAUR_INIT_JOINT_ANGLES", INIT_JOINT_ANGLES) +gin.constant("minitaur_constants.MINITAUR_JOINT_DIRECTIONS", JOINT_DIRECTIONS) +gin.constant("minitaur_constants.MINITAUR_JOINT_OFFSETS", JOINT_OFFSETS) +gin.constant("minitaur_constants.MINITAUR_MOTOR_NAMES", MOTOR_NAMES) +gin.constant("minitaur_constants.MINITAUR_END_EFFECTOR_NAMES", + END_EFFECTOR_NAMES) +gin.constant("minitaur_constants.MINITAUR_MOTOR_GROUP", MOTOR_GROUP) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor.py new file mode 100644 index 000000000..2ec0c927e --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor.py @@ -0,0 +1,171 @@ +"""This file implements an accurate motor model.""" + +import numpy as np + +from pybullet_envs.minitaur.robots import robot_config + +VOLTAGE_CLIPPING = 50 +# TODO(b/73728631): Clamp the pwm signal instead of the OBSERVED_TORQUE_LIMIT. +OBSERVED_TORQUE_LIMIT = 5.7 +MOTOR_VOLTAGE = 16.0 +MOTOR_RESISTANCE = 0.186 +MOTOR_TORQUE_CONSTANT = 0.0954 +MOTOR_VISCOUS_DAMPING = 0 +MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / ( + MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT) +NUM_MOTORS = 8 +MOTOR_POS_LB = 0.5 +MOTOR_POS_UB = 2.5 + + +class MotorModel(object): + """The accurate motor model, which is based on the physics of DC motors. + + The motor model support two types of control: position control and torque + control. In position control mode, a desired motor angle is specified, and a + torque is computed based on the internal motor model. When the torque control + is specified, a pwm signal in the range of [-1.0, 1.0] is converted to the + torque. + + The internal motor model takes the following factors into consideration: + pd gains, viscous friction, back-EMF voltage and current-torque profile. + """ + + def __init__(self, + kp=1.2, + kd=0, + torque_limits=None, + motor_control_mode=robot_config.MotorControlMode.POSITION): + self._kp = kp + self._kd = kd + self._torque_limits = torque_limits + self._motor_control_mode = motor_control_mode + self._resistance = MOTOR_RESISTANCE + self._voltage = MOTOR_VOLTAGE + self._torque_constant = MOTOR_TORQUE_CONSTANT + self._viscous_damping = MOTOR_VISCOUS_DAMPING + self._current_table = [0, 10, 20, 30, 40, 50, 60] + self._torque_table = [0, 1, 1.9, 2.45, 3.0, 3.25, 3.5] + self._strength_ratios = [1.0] * NUM_MOTORS + + def set_strength_ratios(self, ratios): + """Set the strength of each motors relative to the default value. + + Args: + ratios: The relative strength of motor output. A numpy array ranging from + 0.0 to 1.0. + """ + self._strength_ratios = np.array(ratios) + + def set_motor_gains(self, kp, kd): + """Set the gains of all motors. + + These gains are PD gains for motor positional control. kp is the + proportional gain and kd is the derivative gain. + + Args: + kp: proportional gain of the motors. + kd: derivative gain of the motors. + """ + self._kp = kp + self._kd = kd + + def set_voltage(self, voltage): + self._voltage = voltage + + def get_voltage(self): + return self._voltage + + def set_viscous_damping(self, viscous_damping): + self._viscous_damping = viscous_damping + + def get_viscous_dampling(self): + return self._viscous_damping + + def convert_to_torque(self, + motor_commands, + motor_angle, + motor_velocity, + true_motor_velocity, + motor_control_mode=None): + """Convert the commands (position control or pwm control) to torque. + + Args: + motor_commands: The desired motor angle if the motor is in position + control mode. The pwm signal if the motor is in torque control mode. + motor_angle: The motor angle observed at the current time step. It is + actually the true motor angle observed a few milliseconds ago (pd + latency). + motor_velocity: The motor velocity observed at the current time step, it + is actually the true motor velocity a few milliseconds ago (pd latency). + true_motor_velocity: The true motor velocity. The true velocity is used to + compute back EMF voltage and viscous damping. + motor_control_mode: A MotorControlMode enum. + + Returns: + actual_torque: The torque that needs to be applied to the motor. + observed_torque: The torque observed by the sensor. + """ + if not motor_control_mode: + motor_control_mode = self._motor_control_mode + + if (motor_control_mode is robot_config.MotorControlMode.TORQUE) or ( + motor_control_mode is robot_config.MotorControlMode.HYBRID): + raise ValueError( + "{} is not a supported motor control mode".format(motor_control_mode)) + + kp = self._kp + kd = self._kd + + if motor_control_mode is robot_config.MotorControlMode.PWM: + # The following implements a safety controller that softly enforces the + # joint angles to remain within safe region: If PD controller targeting + # the positive (negative) joint limit outputs a negative (positive) + # signal, the corresponding joint violates the joint constraint, so + # we should add the PD output to motor_command to bring it back to the + # safe region. + pd_max = -1 * kp * (motor_angle - MOTOR_POS_UB) - kd / 2. * motor_velocity + pd_min = -1 * kp * (motor_angle - MOTOR_POS_LB) - kd / 2. * motor_velocity + pwm = motor_commands + np.minimum(pd_max, 0) + np.maximum(pd_min, 0) + else: + pwm = -1 * kp * (motor_angle - motor_commands) - kd * motor_velocity + pwm = np.clip(pwm, -1.0, 1.0) + return self._convert_to_torque_from_pwm(pwm, true_motor_velocity) + + def _convert_to_torque_from_pwm(self, pwm, true_motor_velocity): + """Convert the pwm signal to torque. + + Args: + pwm: The pulse width modulation. + true_motor_velocity: The true motor velocity at the current moment. It is + used to compute the back EMF voltage and the viscous damping. + + Returns: + actual_torque: The torque that needs to be applied to the motor. + observed_torque: The torque observed by the sensor. + """ + observed_torque = np.clip( + self._torque_constant * + (np.asarray(pwm) * self._voltage / self._resistance), + -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT) + if self._torque_limits is not None: + observed_torque = np.clip(observed_torque, -1.0 * self._torque_limits, + self._torque_limits) + + # Net voltage is clipped at 50V by diodes on the motor controller. + voltage_net = np.clip( + np.asarray(pwm) * self._voltage - + (self._torque_constant + self._viscous_damping) * + np.asarray(true_motor_velocity), -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING) + current = voltage_net / self._resistance + current_sign = np.sign(current) + current_magnitude = np.absolute(current) + # Saturate torque based on empirical current relation. + actual_torque = np.interp(current_magnitude, self._current_table, + self._torque_table) + actual_torque = np.multiply(current_sign, actual_torque) + actual_torque = np.multiply(self._strength_ratios, actual_torque) + if self._torque_limits is not None: + actual_torque = np.clip(actual_torque, -1.0 * self._torque_limits, + self._torque_limits) + return actual_torque, observed_torque diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor_model_v2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor_model_v2.py new file mode 100644 index 000000000..40927cc7c --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_motor_model_v2.py @@ -0,0 +1,145 @@ +# Lint as: python3 +"""This file implements an accurate motor model.""" + +from typing import Tuple + +import gin +import numpy as np + +from pybullet_envs.minitaur.robots import hybrid_motor_model +from pybullet_envs.minitaur.robots import robot_config + +VOLTAGE_CLIPPING = 50 +# TODO(b/73728631): Clamp the pwm signal instead of the OBSERVED_TORQUE_LIMIT. +OBSERVED_TORQUE_LIMIT = 5.7 +MOTOR_VOLTAGE = 16.0 +MOTOR_RESISTANCE = 0.186 +MOTOR_TORQUE_CONSTANT = 0.0954 +MOTOR_VISCOUS_DAMPING = 0 +MOTOR_POS_LB = 0.5 +MOTOR_POS_UB = 2.5 + + +@gin.configurable +class MinitaurMotorModel(hybrid_motor_model.HybridMotorModel): + """The accurate motor model, which is based on the physics of DC motors. + + The motor model support two types of control: position control and torque + control. In position control mode, a desired motor angle is specified, and a + torque is computed based on the internal motor model. When the torque control + is specified, a pwm signal in the range of [-1.0, 1.0] is converted to the + torque. + + The internal motor model takes the following factors into consideration: + pd gains, viscous friction, back-EMF voltage and current-torque profile. + """ + + def __init__(self, + num_motors: int, + voltage_clipping: float = VOLTAGE_CLIPPING, + observed_torque_limit: float = OBSERVED_TORQUE_LIMIT, + motor_voltage: float = MOTOR_VOLTAGE, + motor_resistance: float = MOTOR_RESISTANCE, + motor_torque_constant: float = MOTOR_TORQUE_CONSTANT, + motor_viscous_damping: float = MOTOR_VISCOUS_DAMPING, + motor_pos_lb: float = MOTOR_POS_LB, + motor_pos_ub: float = MOTOR_POS_UB, + **kwargs): + super(MinitaurMotorModel, self).__init__(num_motors, **kwargs) + self._voltage_clipping = voltage_clipping + self._observed_torque_limit = observed_torque_limit + self._voltage = motor_voltage + self._resistance = motor_resistance + self._torque_constant = motor_torque_constant + self._viscous_damping = motor_viscous_damping + self._motor_pos_lb = motor_pos_lb + self._motor_pos_ub = motor_pos_ub + self._current_table = [0, 10, 20, 30, 40, 50, 60] + self._torque_table = [0, 1, 1.9, 2.45, 3.0, 3.25, 3.5] + + def set_voltage(self, voltage): + self._voltage = voltage + + def get_voltage(self): + return self._voltage + + def set_viscous_damping(self, viscous_damping): + self._viscous_damping = viscous_damping + + def get_viscous_dampling(self): + return self._viscous_damping + + def get_motor_torques( + self, + motor_commands: np.ndarray, + motor_control_mode=None) -> Tuple[np.ndarray, np.ndarray]: + """Convert the commands (position control or pwm control) to torque. + + Args: + motor_commands: The desired motor angle if the motor is in position + control mode. The pwm signal if the motor is in torque control mode. + motor_control_mode: A MotorControlMode enum. + + Returns: + actual_torque: The torque that needs to be applied to the motor. + observed_torque: The torque observed by the sensor. + """ + if not motor_control_mode: + motor_control_mode = self._motor_control_mode + + if (motor_control_mode is robot_config.MotorControlMode.TORQUE) or ( + motor_control_mode is robot_config.MotorControlMode.HYBRID): + raise ValueError( + "{} is not a supported motor control mode".format(motor_control_mode)) + + motor_angle, motor_velocity = self.get_motor_states() + _, true_motor_velocity = self.get_motor_states(latency=0) + + kp = self._kp + kd = self._kd + + pwm = -1 * kp * (motor_angle - motor_commands) - kd * motor_velocity + pwm = np.clip(pwm, -1.0, 1.0) + return self._convert_to_torque_from_pwm(pwm, true_motor_velocity) + + def _convert_to_torque_from_pwm(self, pwm: np.ndarray, + true_motor_velocity: np.ndarray): + """Convert the pwm signal to torque. + + Args: + pwm: The pulse width modulation. + true_motor_velocity: The true motor velocity at the current moment. It is + used to compute the back EMF voltage and the viscous damping. + + Returns: + actual_torque: The torque that needs to be applied to the motor. + observed_torque: The torque observed by the sensor. + """ + observed_torque = np.clip( + self._torque_constant * + (np.asarray(pwm) * self._voltage / self._resistance), + -self._observed_torque_limit, self._observed_torque_limit) + if (self._torque_lower_limits is not None or + self._torque_upper_limits is not None): + observed_torque = np.clip(observed_torque, self._torque_lower_limits, + self._torque_upper_limits) + + # Net voltage is clipped at 50V by diodes on the motor controller. + voltage_net = np.clip( + np.asarray(pwm) * self._voltage - + (self._torque_constant + self._viscous_damping) * + np.asarray(true_motor_velocity), -self._voltage_clipping, + self._voltage_clipping) + current = voltage_net / self._resistance + current_sign = np.sign(current) + current_magnitude = np.absolute(current) + # Saturate torque based on empirical current relation. + actual_torque = np.interp(current_magnitude, self._current_table, + self._torque_table) + actual_torque = np.multiply(current_sign, actual_torque) + actual_torque = np.multiply(self._strength_ratios, actual_torque) + if (self._torque_lower_limits is not None or + self._torque_upper_limits is not None): + actual_torque = np.clip(actual_torque, self._torque_lower_limits, + self._torque_upper_limits) + return observed_torque, actual_torque diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_v2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_v2.py new file mode 100644 index 000000000..34d97a50d --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/minitaur_v2.py @@ -0,0 +1,122 @@ +# Lint as: python3 +"""Pybullet simulation of Minitaur robot.""" +import math +from typing import Dict, Tuple, Union, Text + +from absl import logging +import gin + +from pybullet_envs.minitaur.robots import minitaur_constants +from pybullet_envs.minitaur.robots import quadruped_base +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots import robot_urdf_loader + + +@gin.configurable +class Minitaur(quadruped_base.QuadrupedBase): + """Minitaur simulation model in pyBullet.""" + + def _pre_load(self): + try: + use_constrained_base = gin.query_parameter( + "robot_urdf_loader.RobotUrdfLoader.constrained_base") + except ValueError: + use_constrained_base = False + if use_constrained_base: + logging.warn( + "use_constrained_base is currently not compatible with Minitaur's " + "leg constraints." + ) + + self._urdf_loader = robot_urdf_loader.RobotUrdfLoader( + pybullet_client=self._pybullet_client, + enable_self_collision=True, + urdf_path=minitaur_constants.MINITAUR_URDF_PATH, + init_base_position=minitaur_constants.INIT_POSITION, + init_base_orientation_quaternion=minitaur_constants + .INIT_ORIENTATION_QUAT, + init_base_orientation_rpy=minitaur_constants.INIT_ORIENTATION_RPY, + init_joint_angles=minitaur_constants.INIT_JOINT_ANGLES, + joint_offsets=minitaur_constants.JOINT_OFFSETS, + joint_directions=minitaur_constants.JOINT_DIRECTIONS, + motor_names=minitaur_constants.MOTOR_NAMES, + end_effector_names=minitaur_constants.END_EFFECTOR_NAMES, + user_group=minitaur_constants.MOTOR_GROUP, + ) + + def _on_load(self): + """Add hinge constraint for Minitaur's diamond shaped leg after loading.""" + half_pi = math.pi / 2.0 + knee_angle = -2.1834 + for (leg_id, leg_position) in enumerate(minitaur_constants.LEG_ORDER): + self._pybullet_client.resetJointState( + self._urdf_loader.robot_id, + self._joint_id_dict["motor_" + leg_position + "L_joint"], + self._motor_directions[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState( + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "L_joint"], + self._motor_directions[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState( + self._urdf_loader.robot_id, + self._joint_id_dict["motor_" + leg_position + "R_joint"], + self._motor_directions[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState( + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "R_joint"], + self._motor_directions[2 * leg_id + 1] * knee_angle, + targetVelocity=0) + + if leg_id < 2: + self._pybullet_client.createConstraint( + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "R_joint"], + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], + minitaur_constants.KNEE_CONSTRAINT_POINT_SHORT, + minitaur_constants.KNEE_CONSTRAINT_POINT_LONG) + else: + self._pybullet_client.createConstraint( + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "R_joint"], + self._urdf_loader.robot_id, + self._joint_id_dict["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], + minitaur_constants.KNEE_CONSTRAINT_POINT_LONG, + minitaur_constants.KNEE_CONSTRAINT_POINT_SHORT) + self.receive_observation() + + def _reset_joint_angles(self, + joint_angles: Union[Tuple[float], Dict[Text, + float]] = None, + num_reset_steps: int = 100): + """Resets joint angles of the robot. + + Note that since Minitaur has additional leg constraints on the end + effectors, directly setting joint angles will lead to constraint violation. + Instead, we apply motor commands to move the motors to the desired position. + + Args: + joint_angles: the desired joint angles to reset to. + num_reset_steps: number of reset steps. + """ + if joint_angles is None: + joint_angles = minitaur_constants.INIT_JOINT_ANGLES + actions = joint_angles + if isinstance(joint_angles, dict): + actions = [ + joint_angles[joint_name] + for joint_name in minitaur_constants.JOINT_NAMES + ] + + # TODO(b/157786642): since the simulation clock is not stepped here, this + # reset behaves slightly different compared to the old robot class. + for _ in range(num_reset_steps): + self.apply_action( + actions, motor_control_mode=robot_config.MotorControlMode.POSITION) + self._pybullet_client.stepSimulation() + self.receive_observation() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/object_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/object_controller.py new file mode 100644 index 000000000..2e7dbae81 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/object_controller.py @@ -0,0 +1,810 @@ +# Lint as: python3 +"""Module for controllers of autonomous objects.""" + +import abc +import bisect +import enum +from typing import Any, Dict, Optional, Sequence, Text, Tuple, Union + +from absl import logging +import dataclasses +import gin +import numpy as np + + +# A constant to be passed into act as parameter t for initial value. +INIT_TIME = -1.0 + +# Distance that is deemed close enough in ChaseController. +_EPS_DISTANCE = 1e-4 + +ControllerOutput = Tuple[np.ndarray, np.ndarray, Dict[Text, Any]] + +ANIMATION_FRAME_NUMBER_KEY = "animation_frame_number" + + +class ControllerBase(metaclass=abc.ABCMeta): + """Base class of object controllers. + + Controller is similar to a policy in that its output controls autonomous + object just as policy output controls agent. To reflect this similarity, + get_action(), the function that "commands" to autonomous object, is named + similar to the counterpart in policy. + """ + + @abc.abstractmethod + def get_action(self, + time_sec: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns position, orientation and pose based on time and observations. + + Args: + time_sec: Time since simulation reset in seconds. If time < 0, returns + initial values and ignores observations. + observations: A dict of all observations. + + Returns: + Position, orientation and an extra info dict for robot joints, human + skeletal pose, etc. + """ + + +@gin.configurable +class StationaryController(ControllerBase): + """Controller that keeps constant position and orientation.""" + + def __init__(self, + position: Sequence[float] = None, + orientation: Sequence[float] = None): + self._position = np.array(position if position is not None else (0, 0, 0)) + self._orientation = np.array( + orientation if orientation is not None else (0, 0, 0, 1)) + + def get_action(self, + t: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns constant position orientation.""" + del t, observations + return self._position, self._orientation, {} + + +@gin.configurable +class CircularMotionController(ControllerBase): + """Controller for circular motion. + + The motion trajectory goes around a center in a circle in xy-plane. + """ + + def __init__(self, + center: Sequence[float], + radius: float, + angular_velocity: float = np.pi, + face_travel_direction: bool = False): + """Constructor. + + Args: + center: Center of circular motion, [x, y, z] in meters. + radius: Radius of the circle in meters. + angular_velocity: Angular velocity of motion, unit rad/s, e.g. pi means + completing a circle in 2 sec. + face_travel_direction: If True, object will face direction of motion. + """ + + self._center = np.array(center) + self._radius = radius + self._angular_velocity = angular_velocity + self._face_travel_direction = face_travel_direction + + def get_action(self, + t: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns position on the circle based on time and constant orientation.""" + del observations + + t = max(0.0, t) + position = np.array( + [np.cos(self._angular_velocity * t), + np.sin(self._angular_velocity * t), + 0]) * self._radius + self._center + if self._face_travel_direction: + yaw = self._angular_velocity * t + ( + np.sign(self._angular_velocity) * np.pi / 2) + orientation = np.array((0, 0, np.sin(yaw / 2), np.cos(yaw / 2))) + else: + orientation = np.array((0, 0, 0, 1)) + return position, orientation, {} + + +class PatrolRepeatMode(enum.Enum): + """Enums that defines trajectory repeat mode for patrol type controller.""" + + # Trajectory does not repeat. For 3 points a, b, c, the trajectory moves + # along a -> b -> c and then stops at c forever. + NO_REPEAT = 0 + + # Trajectory repeats as a loop. For 3 points a, b, c, the trajectory moves + # along a -> b -> c -> a -> b ... + LOOP = 1 + + # Trajectory repeats back tracking previous point first. For 3 points a, b, c, + # the trajectory moves along a -> b -> c -> b -> a -> b -> c ... + BACK_TRACK = 2 + + # Trajectory repeats by resetting to the initial position after reaching end. + # For 3 points a, b, c, the trajectory moves a -> b -> c then immediately + # jumps back to a before continue moving along a -> b -> c again. + RESET = 3 + + +@dataclasses.dataclass +class PatrolSegmentData: + """A data class that describes a patrol segment.""" + + # Time in a single cycle to start this segment, range [0, cycle_time). + start_time: float + + # Segment start position. + start_position: np.ndarray + + # Segment velocity vector. + velocity: np.ndarray + + # Orientation quaternion of this segment. + orientation: np.ndarray + + +@gin.configurable +class WayPointPatrolController(ControllerBase): + """Controller for patrolling along define waypoints.""" + + def __init__(self, + points: Sequence[Sequence[float]], + yaw_angle: float = 0, + face_travel_direction: bool = True, + repeat_mode: Union[ + PatrolRepeatMode, Text] = PatrolRepeatMode.NO_REPEAT, + speed_mps: Optional[float] = 1.0, + time_points: Optional[Sequence[float]] = None): + """Constructor. + + Args: + points: List of waypoints, shape Nx3 or Nx2, N is number of points. + yaw_angle: Yaw angle of the object in radians. + face_travel_direction: If True, yaw angle 'zero' will be redefined to be + object's travel direction. Setting yaw_angle to zero with + face_travel_direction == True will results in object always facing its + travel direction. Non-zero yaw_angle will cause additional yaw offsets. + repeat_mode: Behavior of object after reaching the last way point in list. + If the value is Text, it is converted to PatrolRepeatMode. + speed_mps: Speed in meters per second. + time_points: List of times associated with points. These times + represent when the object should arrive at the associated waypoint. + Optional, but if provided it must have the same length as 'points'. + If 'speed_mps' is None, then 'time_points' will be used as-is and there + is no maximum segment speed. If 'speed_mps' is also defined, then it + serves as a maximum speed value and time points which would result in a + segment speed above this value will be altered such that the maximum + segment speed is 'speed_mps'. + """ + self._repeat = (repeat_mode if isinstance(repeat_mode, PatrolRepeatMode) + else PatrolRepeatMode[repeat_mode]) + self._yaw_angle = yaw_angle + self._face_travel_direction = face_travel_direction + self._speed_mps = speed_mps + + if len(points) < 2: + raise ValueError( + f"Need at least two points in 'points', got {len(points)}") + + if time_points is not None and self._repeat is PatrolRepeatMode.LOOP: + raise ValueError("Time points are not compatible with LOOP mode.") + + if (self._repeat is PatrolRepeatMode.NO_REPEAT or + self._repeat is PatrolRepeatMode.RESET): + augmented_points = points + + if time_points is not None: + augmented_time_points = time_points + elif self._repeat is PatrolRepeatMode.LOOP: + augmented_points = list(points) + [points[0]] + elif self._repeat is PatrolRepeatMode.BACK_TRACK: + augmented_points = list(points) + list(reversed(points[:-1])) + + if time_points is not None: + # Time strictly increases, so add the timepoints again on top + # of the last entry. We add the difference between the last time + # and the previous elements (in reverse order), on top of the last + # element where we left off. + augmented_time_points = list(time_points) + \ + list(time_points[-1] + (time_points[-1] - np.array(time_points[1::-1]))) + else: + raise NotImplementedError( + f"Repeat mode {self._repeat} is not supported yet.") + + augmented_points = np.array(augmented_points) + # For Nx2 inputs, pad it to Nx3 with default z value of 0. + if augmented_points.shape[1] == 2: + augmented_points = np.hstack( + augmented_points, np.zeros(augmented_points.shape[0], 1)) + elif augmented_points.shape[1] != 3: + raise ValueError("Expect 'points' to be Nx2 or Nx3.") + + t = 0 + self._segments = [] + + if time_points is None: + for from_point, to_point in zip( + augmented_points[:-1], augmented_points[1:]): + segment, t = self._get_patrol_segment_by_speed(from_point, to_point, t) + self._segments.append(segment) + else: + for from_point, to_point, from_time, to_time in zip( + augmented_points[:-1], augmented_points[1:], + augmented_time_points[:-1], augmented_time_points[1:]): + segment, t = self._get_patrol_segment_by_time(from_point, to_point, t, + t + (to_time - from_time)) + self._segments.append(segment) + + self._segment_times = [l.start_time for l in self._segments] + + self._cycle_time = t + + def _get_patrol_segment_by_time(self, + from_point, + to_point, + from_time, + to_time): + """Returns a PatrolSegmentData for the given points and times.""" + unit_vector, length = self._get_vector(from_point, to_point) + orientation = self._get_orientation(unit_vector) + + if np.isclose(to_time, from_time): + speed_mps = 0 + else: + speed_mps = length / (to_time - from_time) + + if self._speed_mps is not None: + speed_mps = np.min([self._speed_mps, speed_mps]) + + if np.isclose(0, speed_mps): + new_to_time = to_time + else: + new_to_time = np.max([to_time, from_time + (length / speed_mps)]) + + segment = PatrolSegmentData( + start_time=from_time, + start_position=np.array(from_point), + velocity=unit_vector * speed_mps, + orientation=orientation) + + return segment, new_to_time + + def _get_patrol_segment_by_speed(self, from_point, to_point, current_time): + """Returns a PatrolSegmentData for the given points and a constant speed.""" + unit_vector, length = self._get_vector(from_point, to_point) + orientation = self._get_orientation(unit_vector) + + segment = PatrolSegmentData( + start_time=current_time, + start_position=np.array(from_point), + velocity=unit_vector * self._speed_mps, + orientation=orientation) + time = current_time + length / self._speed_mps + + return segment, time + + def _get_vector(self, from_point, to_point): + """Gets the unit vector and length of a from/to point pair.""" + vector = np.array(to_point) - np.array(from_point) + length = np.linalg.norm(vector) + + if length == 0: + raise ValueError(f"Length of patrol segment equal to 0, " + f"from {from_point} to {to_point}.") + unit_vector = vector / length + + return unit_vector, length + + def _get_orientation(self, unit_vector): + """Gets the orientation quaternion given a unit vector.""" + yaw = (np.arctan2(unit_vector[1], unit_vector[0]) + if self._face_travel_direction else 0) + self._yaw_angle + + orientation = np.array((0, 0, np.sin(yaw / 2), np.cos(yaw / 2))) + + return orientation + + def get_action(self, + t: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns position on, and orientation along the patrol segment.""" + del observations + + # t < 0 means initial condition, which is the same as the value at t = 0. + t = max(0, t) + + if t > self._cycle_time: + t = (self._cycle_time if self._repeat is PatrolRepeatMode.NO_REPEAT + else np.fmod(t, self._cycle_time)) + + segment = self._segments[bisect.bisect_right(self._segment_times, t) - 1] + position = ( + segment.start_position + segment.velocity * (t - segment.start_time)) + + return position, segment.orientation.copy(), {} + + +@gin.configurable +class LinearPatrolController(WayPointPatrolController): + """Controller for patrolling along a line segment (back and forth).""" + + def __init__(self, + from_point: Sequence[float], + to_point: Sequence[float], + **kwargs): + """Constructor. + + Args: + from_point: Starting point of motion, [x, y, z] in meters. + to_point: Returning point of motion, [x, y, z] in meters. + **kwargs: Keyword arguments to pass onto base class. + """ + super().__init__([from_point, to_point], + repeat_mode=PatrolRepeatMode.LOOP, + **kwargs) + + +# TODO(b/156126975): migrates this to use difference equation controller. +@gin.configurable +class ChaseController(ControllerBase): + """Controller for an object to chase another object at certain speed.""" + + def __init__(self, + self_key: Text, + target_key: Text, + initial_position: Sequence[float] = (0, 0, 0), + initial_orientation: Sequence[float] = (0, 0, 0, 1), + speed_mps: float = 1.0, + verbose: bool = False): + """Constructor. + + Args: + self_key: Observation dict key of position of object being controlled. + target_key: Observation dict key of position of target object. + initial_position: Initial position of the object. + initial_orientation: Initial orientation of the object in xyzw quaternion. + speed_mps: Speed in meters per second, always positive. + verbose: If True, log details of get_action() calculation for debugging. + """ + self._init_position = np.array(initial_position) + self._init_orientation = np.array(initial_orientation) + self._previous_orientation = self._init_orientation + if speed_mps <= 0: + raise ValueError( + f"'speed_mps' should be a positive value, got {speed_mps}.") + self._speed_mps = speed_mps + + self._self_key = self_key + self._target_key = target_key + self._verbose = verbose + + self._time_sec = 0 + + def get_action(self, + time_sec: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns position and orientation of the object being controlled. + + Args: + time_sec: Time since simulation reset in seconds. If time < 0, returns + initial values and ignores observations. + observations: A dict of all observations. + """ + if time_sec < 0: + # Initializes internal time. + self._time_sec = 0 + return self._init_position.copy(), self._init_orientation.copy(), {} + + self_position = observations[self._self_key] + target_position = observations[self._target_key] + + # Calculates delta vector and projects it to xy-plane. + delta_vector = (target_position - self_position) * (1, 1, 0) + delta_t = time_sec - self._time_sec + + # Advances internal time. + self._time_sec = time_sec + + if self._verbose: + with np.printoptions(precision=3, suppress=True): + logging.info("t = %.1f, self %s: %s, target %s: %s, v: %s, dt %.1f.", + self._t, + self._self_key, observations[self._self_key], + self._target_key, observations[self._target_key], + delta_vector, delta_t) + + # Avoids sigularity when it is close enough. Keeps previous orientation. + distance = np.linalg.norm(delta_vector) + if distance < _EPS_DISTANCE: + return target_position.copy(), self._previous_orientation.copy(), {} + + unit_delta_vector = delta_vector / distance + new_position = (unit_delta_vector * min(self._speed_mps * delta_t, distance) + + self_position) + + new_yaw = np.arctan2(unit_delta_vector[1], unit_delta_vector[0]) + new_orientation = np.array((0, 0, np.sin(new_yaw / 2), np.cos(new_yaw / 2))) + self._previous_orientation = new_orientation + + return new_position, new_orientation.copy(), {} + + +@gin.configurable +class AnimationFrameController(ControllerBase): + """An extra action controller to control playback of animation sequence.""" + + def __init__(self, fps: float = 10.0, + pause_between_repeat_sec: float = 0.0, + start_time_sec: float = 0.0): + """Constructor. + + Args: + fps: Frame per second of animation. + pause_between_repeat_sec: Pause between repeat in second. + start_time_sec: The time when animation starts to play. + """ + self._fps = fps + self._total_length = None + self._pause_between_repeat_sec = pause_between_repeat_sec + self._start_time_sec = start_time_sec + + def set_total_length(self, total_length: int): + """Sets total animation frame length.""" + if total_length <= 0: + raise ValueError( + f"Total number of frame must be >= 0, got {total_length}.") + self._total_length = total_length + + def get_action(self, + time_sec: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Returns animation frame number with default position and orientation. + + Args: + time_sec: Time since simulation reset in seconds. If time < 0, returns + initial values and ignores observations. + observations: A dict of all observations. + """ + time_sec = max(0, time_sec - self._start_time_sec) + frame = int(time_sec * self._fps) + if self._total_length: + frame = frame % ( + self._total_length + int(self._pause_between_repeat_sec * self._fps)) + frame = min(frame, self._total_length - 1) + return np.ndarray((0, 0, 0)), np.ndarray((0, 0, 0, 1)), { + ANIMATION_FRAME_NUMBER_KEY: frame} + + +@gin.configurable +class ConversationController(ControllerBase): + """Controller for an object that mimics conversational behavior. + + A controlled object is arrayed in a conversation about a center point. + When a target object reaches a thresholded distance away from the center, + the controlled object will face the target object and move away from + the target's intended path along an orthogonal direction vector until it + passes. + """ + + def __init__(self, + self_key: Text, + target_key: Text, + position: Sequence[float] = None, + orientation: Sequence[float] = None, + conversation_center: Sequence[float] = None, + proximity_threshold: float = 1.0, + speed_mps: float = 0.1): + """Constructor. + + Args: + self_key: Observation dict key of position of object being controlled. + target_key: Observation dict key of position of target object. + position: Initial position of the object. + orientation: Initial orientation of the object in xyzw quaternion. + conversation_center: Position of the center of the conversation group. + proximity_threshold: The distance from the conversation center that the + target must reach in order to prompt a response from the controlled + object. + speed_mps: Speed in meters per second, always positive. + """ + self._self_key = self_key + self._target_key = target_key + self._position = np.array(position or (0, 0, 0)) + self._orientation = np.array(orientation or (0, 0, 0, 1)) + self._conversation_center = np.array(conversation_center or (0, 0, 0)) + self._proximity_threshold = proximity_threshold + self._speed_mps = speed_mps + + self._prev_target_position = None + self._wait_position = None + + def _get_wait_position(self, self_position, target_position): + """Gets the waiting position for the controlled object. + + This returns the position that the controlled object should move towards + to create physical space such that the target object may pass through + the conversation space. + + Args: + self_position: The current position of the controlled object. + target_position: The current position of the target object. + + Returns: + An xyz position representing the waiting position that the controlled + object should move towards to create space. + """ + # Find an orthogonal projection to the target's path + target_path = np.array(target_position - self._prev_target_position) + self_path = np.array(self_position - self._prev_target_position) + + unit_target_path = target_path / np.linalg.norm(target_path) + + projected_point = np.dot(self_path, unit_target_path) * unit_target_path + projected_point += self._prev_target_position + + # Get the position that lies along the orthogonal projection vector + # but in the opposite direction from the target's path and exactly + # proximity_threshold distance away. + return self._get_position( + projected_point, + self_position, + self._proximity_threshold, + self._proximity_threshold) + + def _get_orientation(self, source_position, target_position): + """Gets orientation required to face target_position from source_position. + + Args: + source_position: The source position where an object would be located. + target_position: The target position that an object should face. + + Returns: + A xyzw quaternion indicating the orientation. + """ + if np.allclose(source_position, target_position): + return self._orientation + + delta_vector = (target_position - source_position) * (1, 1, 0) + + new_yaw = np.arctan2(delta_vector[1], delta_vector[0]) + new_orientation = np.array( + (0, 0, np.sin(new_yaw / 2), np.cos(new_yaw / 2))) + + return new_orientation + + def _get_position(self, + source_position, + target_position, + min_delta, + max_delta): + """Gets the next position along the vector from source to target. + + This returns the position that should be moved to next which lies along + the direction vector from source -> target with a minimum length of + min_delta and a maximum distance of max_delta. + + Args: + source_position: The current position of the controlled object. + target_position: The target position to move to. + min_delta: The minimum amount of distance to move. + max_delta: The maximum amount of distance to move. + + Returns: + An xyz position representing the next position to move to. + """ + delta_vector = (target_position - source_position) * (1, 1, 0) + distance = np.linalg.norm(delta_vector) + + # If the distance to the target is greater than the maximum step delta, + # then normalize the vector and set it to the max step delta. + if distance > max_delta: + delta_vector = (delta_vector / distance) * max_delta + # If the distance is less than the minimum step delta, then normalize + # the vector and set it to the min step delta. + elif distance < min_delta: + delta_vector = (delta_vector / distance) * min_delta + + new_position = (delta_vector + source_position) + + return new_position + + def _get_target_distance_to_center(self, target_position): + """Gets the distance from the target to the conversation center point. + + Args: + target_position: The target position. + + Returns: + The scalar distance from the target position to the conversation center. + """ + # Calculates delta vector and projects it to xy-plane. + delta_vector = (target_position - self._conversation_center) * (1, 1, 0) + + # Compute the length of the delta vector. + return np.linalg.norm(delta_vector) + + def get_action(self, + t: float, + observations: Dict[Text, Any]) -> ControllerOutput: + """Gets the position and orientation of the controlled object. + + Args: + t: The current time step. + observations: Dict containing sensor observations for current time step. + + Returns: + The new position and orientation for the controlled object. + """ + + position = self._position + orientation = self._orientation + + # Observations are only available for positive time steps. + if t >= 0: + self_position = observations[self._self_key] + target_position = observations[self._target_key] + + target_distance = self._get_target_distance_to_center(target_position) + + # Check if the target is within the threshold distance of the + # conversation center. + if(target_distance < self._proximity_threshold and + self._prev_target_position is not None): + + # If it is, get the position that the controlled object should move to + # in order to create space and get the resulting action/orientation. + wait_position = self._get_wait_position(self_position, target_position) + + orientation = self._get_orientation( + self_position, + target_position) + + position = self._get_position( + self_position, + wait_position, + 0.0, + self._speed_mps) + else: + # Otherwise, get the position/orientation required to move back to + # the original position and face the conversation center. + orientation = self._get_orientation( + self_position, + self._conversation_center) + + position = self._get_position( + self_position, + self._position, + 0.0, + self._speed_mps) + + self._prev_target_position = target_position + + # Only return a new position along the x/y axis, z should be unaffected. + position = np.array([position[0], position[1], self._position[2]]) + return position, orientation, {} + + +@gin.configurable +class PauseIfCloseByWrapper(ControllerBase): + """A controller wrapper that pauses controller if object is close to others. + + This wrapper works best if the underlying controller is time based. It is + intended to be a simple way to stop agent when blocked and is not for + reliable collision avoidance. + """ + + _DEFAULT_PAUSE_DISTANCE_M = 1.0 + + def __init__( + self, + wrapped_controller: ControllerBase, + self_pos_key: Text, + others_pos_keys: Sequence[Text], + pause_distance: Union[float, Sequence[float]] = _DEFAULT_PAUSE_DISTANCE_M, + self_yaw_key: Optional[Text] = None, + active_front_sector: Optional[float] = None): + """Constructor. + + Args: + wrapped_controller: The controller being wrapped. + self_pos_key: Observation key of self position. + others_pos_keys: Observation keys of others' positions. + pause_distance: The distance limit before the controller pauses in meters. + Can be a float value which applies to all objects specified in + others_pos_keys or a Sequence of float values with the same length + as others_pos_keys denoting the pause distance for each individual + object in the same order in others_pos_keys. Default pause distance is + one meter. + self_yaw_key: Observation key of self yaw. Required if active_front_sector + is specified. + active_front_sector: If specified, it defines pie-shaped active region in + front of controlled object. The pie-shaped area is symmetric about the + forward direction of controlled object with it angle defined by this + arg in radians, Only when other objects shows up in this region and + pause distance requirement is met, pause is actived. + """ + + self._controller = wrapped_controller + self._pause_start_t = -1 + self._shift_t = 0 + self._last_action = None + self._self_pos_key = self_pos_key + self._others_pos_keys = list(others_pos_keys) # Make a copy. + + if isinstance(pause_distance, float): + pause_distance = [pause_distance] * len(others_pos_keys) + + if len(pause_distance) != len(others_pos_keys): + raise ValueError( + "pause_distance and others_pos_keys must have the same length.") + self._pause_distance = list(pause_distance) # Make a copy. + + if active_front_sector is not None and self_yaw_key is None: + raise ValueError( + "self_yaw_key must be specified if active_front_sector is specified.") + + self._self_yaw_key = self_yaw_key + self._active_front_sector = active_front_sector + + def get_action(self, + t: float, + observations: Dict[Text, Any]) -> ControllerOutput: + + """Gets the position and orientation of the controlled object. + + Args: + t: The current time step. + observations: Dict containing sensor observations for current time step. + + Returns: + The new position and orientation for the controlled object. + """ + if t < 0: + self._pause_start_t = -1 + self._shift_t = 0 + self._last_action = self._controller.get_action( + t, observations) + return self._last_action + + if self._should_pause(observations): + # Only record the start time of pause. + if self._pause_start_t < 0: + self._pause_start_t = t + return self._last_action + else: + if self._pause_start_t >= 0: + self._shift_t += t - self._pause_start_t + self._pause_start_t = -1 + + self._last_action = self._controller.get_action( + t - self._shift_t, observations) + return self._last_action + + def _should_pause(self, observations) -> bool: + """Determines whether the controller should pause.""" + self_position_2d = observations[self._self_pos_key][:2] + for pos_key, pause_distance in zip( + self._others_pos_keys, self._pause_distance): + position_2d = observations[pos_key][:2] + vector_2d = position_2d - self_position_2d + distance = np.linalg.norm(vector_2d) + + if self._active_front_sector is None: + return distance <= pause_distance + else: + yaw = observations[self._self_yaw_key][0] + dot = np.dot(vector_2d / distance, np.array([np.cos(yaw), np.sin(yaw)])) + return (distance <= pause_distance and + np.arccos(dot) < self._active_front_sector / 2) + + return False diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/quadruped_base.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/quadruped_base.py new file mode 100644 index 000000000..960c278ed --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/quadruped_base.py @@ -0,0 +1,724 @@ +# Lint as: python3 +"""The base class for all quadrupeds.""" +from typing import Any, Callable, Dict, Sequence, Tuple, Text, Union +import gin +import gym +import numpy as np + +from pybullet_utils import bullet_client +from pybullet_envs.minitaur.envs_v2.sensors import sensor as sensor_lib +from pybullet_envs.minitaur.robots import hybrid_motor_model +from pybullet_envs.minitaur.robots import robot_base +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots import robot_urdf_loader +from pybullet_envs.minitaur.robots.safety import data_types as safety_data_types +from pybullet_envs.minitaur.robots.utilities import kinematics_utils + +_UNIT_QUATERNION = (0, 0, 0, 1) +_GRAVITY_ACCELERATION_OFFSET = (0, 0, 10) + + +@gin.configurable +class QuadrupedBase(robot_base.RobotBase): + """The basic quadruped class for both sim and real robots.""" + + def __init__( + self, + pybullet_client: bullet_client.BulletClient, + clock: Callable[..., float], + motor_control_mode: robot_config.MotorControlMode, + motor_limits: robot_config.MotorLimits, + motor_model_class: Any = hybrid_motor_model.HybridMotorModel, + action_filter: Any = None, + sensors: Sequence[sensor_lib.Sensor] = (), + safety_config: safety_data_types.SafetyConfig = None, + **kwargs, + ): + """Initializes the class. + + Args: + pybullet_client: The PyBullet client. + clock: The sim or real clock. The clock function is typically provided by + the gym environment. + motor_control_mode: Specifies in which mode the motor operates. + motor_limits: The motor limits of the robot. Used by the motor_model_class + and action space building. + motor_model_class: The motor model to use. Not needed for real robots. + action_filter: The filter to smooth and/or regulate the actions. + sensors: All sensors mounted on the robot. + safety_config: The safety setting for the robot. + **kwargs: Additional args. + """ + + self._pybullet_client = pybullet_client + self._clock = clock + self._motor_control_mode = motor_control_mode + self._motor_model_class = motor_model_class + self._motor_limits = motor_limits + self._action_space = None + self._action_names = None + self._action_filter = action_filter + self._sensors = sensors + self._safety_config = safety_config + self._urdf_loader = None + self._last_base_velocity = np.zeros(3) + self._last_observation_time = self._clock() + self._last_base_acceleration_world = np.zeros(3) + self._last_base_acceleration_accelerometer = np.zeros(3) + + self.load() + + def load( + self, + base_position: Tuple[float] = None, + base_orientation_quaternion: Tuple[float] = None, + joint_angles: Union[Dict[Text, float], Tuple[float]] = None, + ): + """Loads the URDF with the configured pose. + + Args: + base_position: The base position after URDF loading. Will use the + configured pose in gin if None. + base_orientation_quaternion: The base orientation after URDF loading. Will + use the configured values in gin if not specified. + joint_angles: The desired joint angles after loading. Will use the + configured values if None. + """ + # A robot specific pre loading routing. + self._pre_load() + + if not self._urdf_loader: + self._urdf_loader = robot_urdf_loader.RobotUrdfLoader( + pybullet_client=self._pybullet_client) + + # Record the urdf pose at loading, which will be used as the rotation + # reference for base rotation computation. + self._init_urdf_position, self._init_orientation_quat = ( + self._pybullet_client.getBasePositionAndOrientation( + self._urdf_loader.robot_id)) + unused_position, self._init_orientation_inv_quat = ( + self._pybullet_client.invertTransform( + position=(0, 0, 0), orientation=self._init_orientation_quat)) + + # Joint ids may be different from the motor ids. + self._joint_id_dict = self._urdf_loader.get_joint_id_dict() + for joint_id in self._joint_id_dict.values(): + # Disables the default motors in PyBullet. + self._pybullet_client.setJointMotorControl2( + bodyIndex=self._urdf_loader.robot_id, + jointIndex=joint_id, + controlMode=self._pybullet_client.VELOCITY_CONTROL, + targetVelocity=0, + force=0) + # Removes the default joint damping in PyBullet. + self._pybullet_client.changeDynamics( + self._urdf_loader.robot_id, + joint_id, + linearDamping=0, + angularDamping=0) + + # We expect that this is non-empty for all quadrupedes, and should be an + # OrderedDict. + self._motor_id_dict = self._urdf_loader.get_motor_id_dict() + if not self._motor_id_dict: + raise ValueError("Motor id dict cannot be empty for quadrupeds.") + self._motor_ids = self._motor_id_dict.values() + self._num_motors = len(self._motor_id_dict) + + self._build_action_space() + + # Not needed for real robots. + if self._motor_model_class: + # TODO(b/151664871): Also supports position/velocity limits in the motor + # model. + self._motor_model = self._motor_model_class( + num_motors=self._num_motors, + motor_control_mode=self._motor_control_mode, + torque_lower_limits=self._motor_limits.torque_lower_limits, + torque_upper_limits=self._motor_limits.torque_upper_limits, + ) + + # Caches the variable for faster computation during stepping. + self._motor_direction_dict = self._urdf_loader.get_joint_direction_dict( + self._motor_id_dict.keys()) + self._motor_directions = np.array(list(self._motor_direction_dict.values())) + + self._motor_offset_dict = self._urdf_loader.get_joint_offset_dict( + self._motor_id_dict.keys()) + self._motor_offsets = np.array(list(self._motor_offset_dict.values())) + + # A robot specific routine post loading. + self._on_load() + + # Robot sensors may use information from the class. So we initialize them + # after the loading is done. + for sensor in self._sensors: + sensor.set_robot(self) + + def _build_action_space(self): + """Builds the action space of the robot using the motor limits.""" + if self._motor_control_mode == robot_config.MotorControlMode.POSITION: + self._action_space = gym.spaces.Box( + low=self._motor_limits.angle_lower_limits, + high=self._motor_limits.angle_upper_limits, + shape=(self._num_motors,), + dtype=np.float32) # TODO(b/159160184) Make dtype configurable. + self._action_names = tuple( + "POSITION_{}".format(motor) for motor in self._motor_id_dict.keys()) + elif self._motor_control_mode == robot_config.MotorControlMode.TORQUE: + self._action_space = gym.spaces.Box( + low=self._motor_limits.torque_lower_limits, + high=self._motor_limits.torque_upper_limits, + shape=(self._num_motors,), + dtype=np.float32) + self._action_names = tuple( + "TORQUE_{}".format(motor) for motor in self._motor_id_dict.keys()) + elif self._motor_control_mode == robot_config.MotorControlMode.HYBRID: + hybrid_action_limits_low = [ + self._motor_limits.angle_lower_limits, # q + # q_dot + self._motor_limits.velocity_lower_limits, + 0, # kp + 0, # kd + self._motor_limits.torque_lower_limits + ] # tau + hybrid_action_limits_high = [ + self._motor_limits.angle_upper_limits, + self._motor_limits.velocity_upper_limits, np.inf, np.inf, + self._motor_limits.torque_upper_limits + ] + space_low = np.full( + (self._num_motors, robot_config.HYBRID_ACTION_DIMENSION), + hybrid_action_limits_low).ravel() + space_high = np.full( + (self._num_motors, robot_config.HYBRID_ACTION_DIMENSION), + hybrid_action_limits_high).ravel() + self._action_space = gym.spaces.Box( + low=space_low, high=space_high, dtype=np.float32) + self._action_names = tuple( + "HYBRID_{}".format(motor) for motor in self._motor_id_dict.keys()) + else: + raise NotImplementedError("Not yet implemented!") + + def _pre_load(self): + """Robot specific pre load routine. + + For example, this allows configuration of the URDF loader. + """ + pass + + def _on_load(self): + """Robot specific post load routine. + + For example, we need to add add additional hinge constraints to the leg + components of Minitaur after loading. + + """ + pass + + @gin.configurable + def reset( + self, + base_position: Tuple[float] = None, + base_orientation_quaternion: Tuple[float] = None, + joint_angles: Union[Dict[Text, float], Tuple[float]] = None, + save_base_pose: bool = False, + **kwargs, + ): + """Resets the robot base and joint pose without reloading the URDF. + + Base pose resetting only works for simulated robots or visualization of real + robots. This routine also updates the initial observation dict. + + Args: + base_position: The desired base position. Will use the configured pose in + gin if None. Does not affect the position of the real robots in general. + base_orientation_quaternion: The base orientation after resetting. Will + use the configured values in gin if not specified. + joint_angles: The desired joint angles after resetting. Will use the + configured values if None. + save_base_pose: Save the base position and orientation as the default pose + after resetting. + **kwargs: Other args for backward compatibility. TODO(b/151975607): Remove + after migration. + """ + # Reset the robot's motor model. + self._motor_model.reset() + + # Reset the quantities for computing base acceleration. + self._last_base_velocity = np.zeros(3) + self._last_observation_time = self._clock() + self._last_base_acceleration_world = np.zeros(3) + self._last_base_acceleration_accelerometer = np.zeros(3) + + # Solves chicken and egg problem. We need to run a control step to obtain + # the first motor torques. + self._motor_torques = np.zeros(self._num_motors) + + # Receives a set of observation from the robot in case the reset function + # needs to use them. + self.receive_observation() + + self._reset_base_pose(base_position, base_orientation_quaternion) + self._reset_joint_angles(joint_angles) + + if save_base_pose: + # Records the base pose at resetting again, in case Reset is called with a + # different base orientation. This base pose will be used as zero + # rotation reference for base rotation computation. + self._init_urdf_position, self._init_orientation_quat = ( + self._pybullet_client.getBasePositionAndOrientation( + self._urdf_loader.robot_id)) + unused_position, self._init_orientation_inv_quat = ( + self._pybullet_client.invertTransform( + position=(0, 0, 0), orientation=self._init_orientation_quat)) + + # Updates the observation at the end of resetting. + self.receive_observation() + self._time_at_reset = self._clock() + + def GetTimeSinceReset(self): + return self._clock() - self._time_at_reset + + def _reset_base_pose(self, position=None, orientation_quat=None): + """Resets the pose of the robot's base. + + Base pose resetting only works for simulated robots or visualization of real + robots. + + Args: + position: The desired base position. Will use the configured pose in gin + if None. + orientation_quat: The desired base rotation. Will use the configured + default pose in None. + """ + self._urdf_loader.reset_base_pose(position, orientation_quat) + + def _reset_joint_angles(self, + joint_angles: Union[Tuple[float], + Dict[Text, float]] = None): + """Resets the joint pose. + + Real robots need to specify their routine to send joint angles. Simulated + Minitaur robots also needs to use dynamics to drive the motor joints, due to + the additional hinge joints not present in the URDF. + + Args: + joint_angles: The joint pose if provided. Will use the robot default pose + from configuration. + """ + # TODO(b/148897311): Supports tuple as the input. + self._urdf_loader.reset_joint_angles(joint_angles) + + def terminate(self): + """The safe exit routine for the robot. + + Only implemented for real robots. + + """ + pass + + def step(self, action: Any, num_sub_steps: int = 1): + """Steps the simulation. + + This is maintained for backward compatibility with the old robot class. + + Args: + action: The control command to be executed by the robot. + num_sub_steps: Each action can be applied (possibly with interpolation) + multiple timesteps, to simulate the elapsed time between two consecutive + commands on real robots. + """ + action = self.pre_control_step(action) + + for _ in range(num_sub_steps): + # TODO(b/149252003): Add sub sampling. + self.apply_action(action) + # Timestep is pre-determined at simulation setup. + self._pybullet_client.stepSimulation() + self.receive_observation() + + self.post_control_step() + + def pre_control_step(self, action: Any, control_timestep: float = None): + """Processes the action and updates per control step quantities. + + Args: + action: The input control command. + control_timestep: The control time step in the environment. + TODO(b/153835005), we can remove this once we pass env to the robot. + + Returns: + The filtered action. + """ + if self._action_filter: + # We assume the filter will create a set of interpolated results. + action = self._action_filter.filter(action) + return action + + def apply_action(self, motor_commands, motor_control_mode=None): + + # TODO(b/148897311): Supports dict in the future. + motor_commands = np.asarray(motor_commands) + + # We always use torque based control at the lowest level for quadrupeds. + unused_observed_torques, actual_torques = ( + self._motor_model.get_motor_torques(motor_commands, motor_control_mode)) + self._motor_torques = actual_torques + + # Converts the motor torques to URDF joint space, which may have different + # directions. + applied_motor_torques = np.multiply(actual_torques, self._motor_directions) + + self._pybullet_client.setJointMotorControlArray( + bodyIndex=self._urdf_loader.robot_id, + jointIndices=self._motor_ids, + controlMode=self._pybullet_client.TORQUE_CONTROL, + forces=applied_motor_torques) + + def _get_base_roll_pitch_yaw_rate(self): + _, angular_velocity = self._pybullet_client.getBaseVelocity( + self._urdf_loader.robot_id) + return kinematics_utils.rotate_to_base_frame( + self._pybullet_client, self.urdf_loader.robot_id, angular_velocity, + self._init_orientation_inv_quat) + + def _get_base_velocity(self): + base_velocity, _ = self._pybullet_client.getBaseVelocity( + self._urdf_loader.robot_id) + return base_velocity + + def _update_base_acceleration(self): + """Update the base acceleration using finite difference.""" + if self._last_observation_time < self.timestamp: + self._last_base_acceleration_world = ( + np.array(self._base_velocity) - self._last_base_velocity) / ( + self.timestamp - self._last_observation_time) + _, inv_base_orientation = self.pybullet_client.invertTransform( + np.zeros(3), np.array(self.base_orientation_quaternion)) + + # An offset is added to the acceleration measured in the world frame + # because the accelerometer reading is in the frame of free-falling robot. + base_acceleration_accelerometer = self.pybullet_client.multiplyTransforms( + np.zeros(3), inv_base_orientation, + self._last_base_acceleration_world + _GRAVITY_ACCELERATION_OFFSET, + _UNIT_QUATERNION)[0] + self._last_base_acceleration_accelerometer = np.array( + base_acceleration_accelerometer) + + def receive_observation(self): + """Receives the observations for all sensors.""" + # Update the intrinsic values including the joint angles, joint + # velocities, and imu readings. + self._base_position, base_orientation_quat = ( + self._pybullet_client.getBasePositionAndOrientation( + self._urdf_loader.robot_id)) + _, self._base_orientation_quat = self._pybullet_client.multiplyTransforms( + positionA=(0, 0, 0), + orientationA=self._init_orientation_inv_quat, + positionB=(0, 0, 0), + orientationB=base_orientation_quat) + self._base_velocity = self._get_base_velocity() + self._base_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion( + self._base_orientation_quat) + + self._base_roll_pitch_yaw_rate = self._get_base_roll_pitch_yaw_rate() + + self._joint_states = self._pybullet_client.getJointStates( + self._urdf_loader.robot_id, self._motor_ids) + self._motor_angles = np.array( + [joint_state[0] for joint_state in self._joint_states]) + self._motor_angles = (self._motor_angles - + self._motor_offsets) * self._motor_directions + + self._motor_velocities = np.array( + [joint_state[1] for joint_state in self._joint_states]) + self._motor_velocities = self._motor_velocities * self._motor_directions + + # We use motor models to track the delayed motor positions and velocities + # buffer. + if self._motor_model: + self._motor_model.update(self._clock(), self._motor_angles, + self._motor_velocities) + + self._update_base_acceleration() + # Update the latest base velocity and timestamp at the end of the API. + self._last_base_velocity = np.array(self._base_velocity) + self._last_observation_time = self.timestamp + + def post_control_step(self): + """Called at the end of a control step outside the action repeat loop.""" + pass + + # TODO(tingnan): Change from "foot_positions" to "feet_positions". + def motor_angles_from_foot_positions(self, + foot_positions, + position_in_world_frame=False): + """Use IK to compute the motor angles, given the feet links' positions. + + Args: + foot_positions: The foot links' positions in frame specified by the next + parameter. The input is a numpy array of size (4, 3). + position_in_world_frame: Whether the foot_positions are specified in the + world frame. + + Returns: + A tuple. The position indices and the angles for all joints along the + leg. The position indices is consistent with the joint orders as returned + by GetMotorAngles API. + """ + joint_position_idxs = np.arange(self.num_motors) + foot_link_ids = tuple(self._urdf_loader.get_end_effector_id_dict().values()) + joint_angles = kinematics_utils.joint_angles_from_link_positions( + pybullet_client=self.pybullet_client, + urdf_id=self.robot_id, + link_positions=foot_positions, + link_ids=foot_link_ids, + joint_dof_ids=joint_position_idxs, + positions_are_in_world_frame=position_in_world_frame) + joint_angles = np.multiply( + np.asarray(joint_angles) - np.asarray(self._motor_offsets), + self._motor_directions) + return joint_position_idxs, joint_angles + + # TODO(tingnan): Change from "foot_positions" to "feet_positions". + def foot_positions(self, position_in_world_frame=False): + """Returns the robot's foot positions in the base/world frame.""" + foot_positions = [] + foot_link_ids = tuple(self._urdf_loader.get_end_effector_id_dict().values()) + for foot_id in foot_link_ids: + if not position_in_world_frame: + foot_positions.append( + kinematics_utils.link_position_in_base_frame( + pybullet_client=self.pybullet_client, + urdf_id=self.robot_id, + link_id=foot_id, + )) + else: + foot_positions.append( + kinematics_utils.link_position_in_world_frame( + pybullet_client=self.pybullet_client, + urdf_id=self.robot_id, + link_id=foot_id, + )) + return np.array(foot_positions) + + def feet_contact_forces(self) -> Sequence[np.ndarray]: + """Gets the contact forces on all feet. + + Reals robot may use a robot specific implementation. For example, the + Laikago will measure each contact force in the corresponding foot's local + frame, and this force will not be the total contact force due to the sensor + limitation. + + For simulated robots, we wll always report the force in the base frame. + + Returns: + A list of foot contact forces. + """ + foot_link_ids = tuple(self._urdf_loader.get_end_effector_id_dict().values()) + contact_forces = [np.zeros(3) for _ in range(len(foot_link_ids))] + + all_contacts = self._pybullet_client.getContactPoints( + bodyA=self._urdf_loader.robot_id) + + for contact in all_contacts: + (unused_flag, body_a_id, body_b_id, link_a_id, unused_link_b_id, + unused_pos_on_a, unused_pos_on_b, contact_normal_b_to_a, unused_distance, + normal_force, friction_1, friction_direction_1, friction_2, + friction_direction_2) = contact + + # Ignore self contacts + if body_b_id == body_a_id: + continue + + if link_a_id in foot_link_ids: + normal_force = np.array(contact_normal_b_to_a) * normal_force + friction_force = np.array(friction_direction_1) * friction_1 + np.array( + friction_direction_2) * friction_2 + force = normal_force + friction_force + local_force = kinematics_utils.rotate_to_base_frame( + self._pybullet_client, self.urdf_loader.robot_id, force, + self._init_orientation_inv_quat) + local_force_norm = np.linalg.norm(local_force) + toe_link_order = foot_link_ids.index(link_a_id) + if local_force_norm > 0: + contact_forces[toe_link_order] += local_force + else: + continue + return contact_forces + + def compute_jacobian_for_one_leg(self, leg_id: int) -> np.ndarray: + """Compute the Jacobian for a given leg. + + Args: + leg_id: Index of the leg for which the jacobian is computed. + + Returns: + The 3 x N transposed Jacobian matrix. where N is the total DoFs of the + robot. For a quadruped, the first 6 columns of the matrix corresponds to + the CoM translation and rotation. The columns corresponds to a leg can be + extracted with indices [6 + leg_id * 3: 6 + leg_id * 3 + 3]. Note that + the jacobian is calculated for motors, which takes motor directions into + consideration. + """ + com_dof = self._urdf_loader.com_dof + foot_link_ids = tuple(self._urdf_loader.get_end_effector_id_dict().values()) + return kinematics_utils.compute_jacobian( + pybullet_client=self.pybullet_client, + urdf_id=self.robot_id, + link_id=foot_link_ids[leg_id], + all_joint_positions=[ + state[0] for state in self._joint_states + ]) * np.concatenate([np.ones(com_dof), self._motor_directions]) + + def map_contact_force_to_joint_torques( + self, leg_id: int, contact_force: np.ndarray) -> Dict[int, float]: + """Maps the foot contact force to the leg joint torques. + + Args: + leg_id: Index of the leg for which the jacobian is computed. + contact_force: Desired contact force experted by the leg. + + Returns: + A dict containing the torques for each motor on the leg. + """ + foot_link_ids = tuple(self._urdf_loader.get_end_effector_id_dict().values()) + jv = self.compute_jacobian_for_one_leg(leg_id) + all_motor_torques = np.matmul(contact_force, jv) + motor_torques = {} + motors_per_leg = self.num_motors // len(foot_link_ids) + com_dof = self._urdf_loader.com_dof + for joint_id in range(leg_id * motors_per_leg, + (leg_id + 1) * motors_per_leg): + motor_torques[joint_id] = all_motor_torques[com_dof + joint_id] + + return motor_torques + + @classmethod + def get_constants(cls): + raise NotImplementedError("Not yet implemented!") + + @property + def timestamp(self): + return self._clock() + + @property + def action_space(self): + return self._action_space + + @property + def action_names(self): + return self._action_names + + @property + def base_orientation_quaternion(self): + """Gets the base orientation as a quaternion. + + The base orientation is always relative to the init_orientation, which + can be updated by Reset function. This is necessary as many URDF can have an + internal frame that is not z-up, so if we don't provide an init_orientation + (through Reset), the loaded robot can have its belly facing the horizontal + direction. + + Returns: + The base orientation in quaternion. + """ + return self._base_orientation_quat + + @property + def base_orientation_quaternion_default_frame(self): + """Gets the base orientation in the robot's default frame. + + This is the base orientation in whatever frame the robot specifies. For + simulated robot this is the URDF's internal frame. For real robot this can + be based on the rpy reading determined by the IMU. + + Returns: + The base orientation in quaternion in a robot default frame. + """ + _, base_orientation_quat = ( + self._pybullet_client.getBasePositionAndOrientation( + self._urdf_loader.robot_id)) + return base_orientation_quat + + @property + def sensors(self): + return self._sensors + + @property + def base_roll_pitch_yaw(self): + return self._base_roll_pitch_yaw + + @property + def base_roll_pitch_yaw_rate(self): + return self._base_roll_pitch_yaw_rate + + @property + def base_position(self): + return self._base_position + + @property + def base_velocity(self): + return self._base_velocity + + @property + def is_safe(self): + return True + + @property + def num_motors(self): + return self._num_motors + + @property + def motor_model(self): + return self._motor_model + + @property + def motor_limits(self) -> robot_config.MotorLimits: + return self._motor_limits + + @property + def motor_angles(self): + return self._motor_angles + + @property + def motor_velocities(self): + return self._motor_velocities + + @property + def motor_torques(self): + return self._motor_torques + + @property + def pybullet_client(self): + return self._pybullet_client + + @property + def urdf_loader(self): + return self._urdf_loader + + @property + def robot_id(self): + return self._urdf_loader.robot_id + + @property + def initital_orientation_inverse_quaternion(self): + return self._init_orientation_inv_quat + + @property + def base_acceleration_accelerometer(self): + """Get the base acceleration measured by an accelerometer. + + The acceleration is measured in the local frame of a free-falling robot, + which is consistent with the robot's IMU measurements. Here the + gravitational acceleration is first added to the acceleration in the world + frame, which is then converted to the local frame of the robot. + + """ + return np.array(self._last_base_acceleration_accelerometer) + + @property + def base_acceleration(self): + """Get the base acceleration in the world frame.""" + return np.array(self._last_base_acceleration_world) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_base.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_base.py new file mode 100644 index 000000000..75dc127cb --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_base.py @@ -0,0 +1,133 @@ +# Lint as: python3 +"""The abstract robot class.""" + +import abc +from typing import Optional, Sequence + +# Action names for robots operating kinematically. +LINEAR_VELOCITY = "linear_velocity" +ANGULAR_VELOCITY = "angular_velocity" + + +class RobotBase(metaclass=abc.ABCMeta): + """The base class for all robots used in the mobility team.""" + + @abc.abstractmethod + def reset( + self, + base_position: Optional[Sequence[float]] = None, + base_orientation_quaternion: Optional[Sequence[float]] = None) -> None: + """Resets the states (e.g. pose and sensor readings) of the robot. + + This is called at the start of each episode by the environment. + + Args: + base_position: Robot base position after reset. If None, robot stay where + it was after reset. For robot that does not support reset with position + change, a ValueError should be raised. + base_orientation_quaternion: Robot base orientation after reset. If None, + robot stays in pre-reset orientation. For robot that does not support + reset with orientation change, a ValueError should be raised. + """ + pass + + @abc.abstractmethod + def terminate(self): + """Shuts down the robot.""" + pass + + @abc.abstractmethod + def pre_control_step(self, action): + """Processes the input action before the action repeat loop. + + We assume that an action sent to the real robot is sticky, i.e. it will be + executed until a new action is received after some time. To simulate this, + we introduced the action_repeat parameter, to reflect how many time steps it + takes for the policy to generate a new action. That is, for each control + step, the simulation contains an inner loop: + + robot.pre_control_step(action) # Smooth or interpolate the action + for i in range(action_repeat): + robot.apply_action(action) + bullet.stepSimulation(time_step) # Step the sim for one time step + robot.receive_observation() # Update the sensor observations + robot.post_control_step() # Update some internal variables. + + Args: + action: Data type depends on the robot. Can be desired motor + position/torques for legged robots, or desired velocity/angular velocity + for wheeled robots. + """ + pass + + @abc.abstractmethod + def apply_action(self, action): + """Applies the action to the robot.""" + pass + + @abc.abstractmethod + def receive_observation(self): + """Updates the robot sensor readings.""" + pass + + @abc.abstractmethod + def post_control_step(self): + """Updates some internal variables such as step counters.""" + pass + + @property + def action_space(self): + """The action spec of the robot.""" + raise NotImplementedError("action_space is not implemented") + + @property + @abc.abstractmethod + def action_names(self): + """Name of each action in the action_space. + + This is a structure of strings with the same shape as the action space, + where each string describes the corresponding element of the action space + (for example, a kinematic robot might return ("linear_velocity", + "angular_velocity")). Used for logging in the safety layer. + """ + + @property + def sensors(self): + """Returns the sensors on this robot. + + Sensors are the main interface between the robot class and the gym + environment. Sensors can return what the robot can measure (e.g. + joint angles, IMU readings), and can represent more general quantities, i.e. + the last action taken, that can be part of the observation space. + Sensor classes are used by the robot class to the specify its observation + space. + + """ + raise NotImplementedError("sensors property not implemented") + + @property + def base_orientation_quaternion(self): + """Returns the base pose as a quaternion in format (x, y, z, w). + + These properties differ from the sensor interfaces, as they represent + the built-in measurable quantities. We assume most robots have an IMU at + its base to measure the base pose. Actually, some sensor classes like the + base pose sensor and joint angle sensor will call these built-in methods. In + general, how these quantities can be extracted depends on the specific real + robots. + + """ + raise NotImplementedError("base_orientation_quaternion is not implemented") + + @property + def base_roll_pitch_yaw(self): + """Returns the base roll, pitch, and yaw angles.""" + raise NotImplementedError("base_roll_pitch_yaw is not implemented") + + @property + def base_roll_pitch_yaw_rate(self): + raise NotImplementedError("base_roll_pitch_yaw_rate is not implemented") + + @property + def base_position(self): + raise NotImplementedError("base_position is not implemented") diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_config.py new file mode 100644 index 000000000..255e686d2 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_config.py @@ -0,0 +1,105 @@ +# Lint as: python3 +"""The configuration parameters for our robots.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import enum +from typing import Sequence, Union +import dataclasses +import gin +import numpy as np + + +@gin.constants_from_enum +class MotorControlMode(enum.Enum): + """The supported motor control modes.""" + POSITION = 1, + + # Apply motor torques directly. + TORQUE = 2, + + # Apply a tuple (q, qdot, kp, kd, tau) for each motor. Here q, qdot are motor + # position and velocities. kp and kd are PD gains. tau is the additional + # motor torque. This is the most flexible control mode. + HYBRID = 3, + + # PWM mode is only availalbe for Minitaur + PWM = 4 + + +# TODO(b/127675924): Group other parameters in the named attrib class. + +# Each hybrid action is a tuple (position, position_gain, velocity, +# velocity_gain, torque) +HYBRID_ACTION_DIMENSION = 5 + + +class HybridActionIndex(enum.Enum): + # The index of each component within the hybrid action tuple. + POSITION = 0 + POSITION_GAIN = 1 + VELOCITY = 2 + VELOCITY_GAIN = 3 + TORQUE = 4 + + +@gin.configurable +class MotorLimits(object): + """The data class for motor limits.""" + + def __init__( + self, + angle_lower_limits: Union[float, Sequence[float]] = float('-inf'), + angle_upper_limits: Union[float, Sequence[float]] = float('inf'), + velocity_lower_limits: Union[float, Sequence[float]] = float('-inf'), + velocity_upper_limits: Union[float, Sequence[float]] = float('inf'), + torque_lower_limits: Union[float, Sequence[float]] = float('-inf'), + torque_upper_limits: Union[float, Sequence[float]] = float('inf'), + ): + """Initializes the class.""" + self.angle_lower_limits = angle_lower_limits + self.angle_upper_limits = angle_upper_limits + self.velocity_lower_limits = velocity_lower_limits + self.velocity_upper_limits = velocity_upper_limits + self.torque_lower_limits = torque_lower_limits + self.torque_upper_limits = torque_upper_limits + + +@gin.constants_from_enum +class WheeledRobotControlMode(enum.Enum): + """The control mode for wheeled robots.""" + # Controls the base of the robot (i.e. in kinematic mode.) or the base wheels + # using motor commands. + BASE = 1 + # Controls arm only + ARM = 2 + # Controls both base and arm + BASE_AND_ARM = 3 + # Controls both base and head + BASE_AND_HEAD = 4 + # Controls the non-wheel motors. This include arms and heads. + BODY = 5 + # Controls all degrees of freedom, i.e. the base and arm/head simultaneously. + ALL = 6 + # High-level navigation target. + NAVIGATION_TARGET = 7 + # Individually addressable actions for body joints, with nested dict actions. + ADDRESSABLE = 8 + + +@dataclasses.dataclass +class TwistActionLimits: + """The data class for twist action limits. + + Common abbreviations used in variable names suffix: + mps = Meters per Second + rps = Radians per Second + """ + max_linear_mps: float + min_linear_mps: float + max_angular_rps: float + min_angular_rps: float + + + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_urdf_loader.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_urdf_loader.py new file mode 100644 index 000000000..2d4bf710a --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/robot_urdf_loader.py @@ -0,0 +1,371 @@ +# Lint as: python3 +"""Helper class to load and manage the robot URDF file in simulation.""" + +import collections +from typing import Dict, Text, Tuple + +import gin +import numpy as np + +from pybullet_utils import bullet_client + +# Base link does not have a parent joint. So we just use the string "robot_base" +# for reference. The corresponding link/joint id is always -1 in pybullet. +ROBOT_BASE = "robot_base" + + +def _sub_dict(joint_name_to_id: Dict[Text, int], + joint_names: Tuple[Text]) -> Dict[Text, int]: + sub_dict = collections.OrderedDict() + if joint_names is None: + return sub_dict + for name in joint_names: + sub_dict[name] = joint_name_to_id[name] + return sub_dict + + +def convert_to_urdf_joint_angles( + robot_space_joint_angles: np.ndarray, + joint_offsets: np.ndarray, + joint_directions: np.ndarray, +): + return robot_space_joint_angles * joint_directions + joint_offsets + + +def convert_to_robot_joint_angles( + urdf_space_joint_angles: np.ndarray, + joint_offsets: np.ndarray, + joint_directions: np.ndarray, +): + return (urdf_space_joint_angles - joint_offsets) * joint_directions + + +@gin.configurable +class RobotUrdfLoader(object): + """A abstract class to manage the robot urdf in sim.""" + + def __init__( + self, + pybullet_client: bullet_client.BulletClient, + urdf_path: Text, + constrained_base: bool = False, + enable_self_collision: bool = True, + init_base_position: Tuple[float] = (0, 0, 0), + init_base_orientation_quaternion: Tuple[float] = None, + init_base_orientation_rpy: Tuple[float] = None, + base_names: Tuple[Text] = None, + init_joint_angles: Dict[Text, float] = None, + joint_offsets: Dict[Text, float] = None, + joint_directions: Dict[Text, int] = None, + motor_names: Tuple[Text] = None, + end_effector_names: Tuple[Text] = None, + user_group: Dict[Text, Tuple[Text]] = None, + ): + """Initialize the class. + + Args: + pybullet_client: A pybullet client. + urdf_path: The path to the URDF to load. + constrained_base: Whether to create a FIXED constraint to the base of the + URDF. Needs to be True for kinematic robots. This allows us to "hang" + the simulated robot in air, and the hanging point can follow arbitrary + provided paths. + enable_self_collision: Determines if the robot can collide with itself. + init_base_position: The base x, y, z after loading. + init_base_orientation_quaternion: The base rotation after loading. + init_base_orientation_rpy: The base rotation after loading, presented in + roll, pitch, yaw. + base_names: The base joint names. Used to find additional links that + belong to the base. Optional because the base might only contain a + single mesh/block, which always has the id of "-1". + init_joint_angles: Maps joint name to the desired joint pose after loading + URDF. If not provided, will use the URDF default. This can be a subset + of all joints in the URDF. This should be in the robot joint convention, + which can be different from the URDF convention. + joint_offsets: The "zero" position of joint angles in the URDF space. + joint_directions: To convert between robot sdk/control and urdf joint + convention. + motor_names: The motor joint names in the URDF. Typically a subset of all + movable joints/DoFs. + end_effector_names: A subset of joints specifying the end-effector + joint(s). For example for legged robots the end effectors are the toe + joints (if provided). For arms this group includes left and right + grippers. + user_group: User defined joint groups. For example for quadrupeds, we may + want to organize all joints according to which leg they belong to. + """ + self._pybullet_client = pybullet_client + self._urdf_path = urdf_path + self._init_base_position = init_base_position + if init_base_orientation_quaternion is not None: + self._init_base_orientation_quaternion = init_base_orientation_quaternion + else: + if init_base_orientation_rpy is None: + raise ValueError("Either init_base_orientation_quaterion " + "or init_base_orientation_rpy is required") + self._init_base_orientation_quaternion = ( + self._pybullet_client.getQuaternionFromEuler( + init_base_orientation_rpy)) + self._constrained_base = constrained_base + self._enable_self_collision = enable_self_collision + self._base_names = base_names + self._init_joint_angles = init_joint_angles + self._joint_offsets = joint_offsets + self._joint_directions = joint_directions + self._motor_names = motor_names + self._end_effector_names = end_effector_names + self._user_group = user_group + self.load( + enable_self_collision=enable_self_collision, + init_base_position=init_base_position, + init_base_orientation_quaternion=init_base_orientation_quaternion, + init_joint_angles=init_joint_angles) + + def get_base_id_dict(self, name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._base_dict + return _sub_dict(self._base_dict, name) + + def get_joint_id_dict(self, name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._joint_name_to_id + return _sub_dict(self._joint_name_to_id, name) + + def get_link_id_dict(self, name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._link_name_to_id + return _sub_dict(self._link_name_to_id, name) + + def get_motor_id_dict(self, name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._motor_dict + return _sub_dict(self._motor_dict, name) + + def get_joint_direction_dict(self, + name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._joint_directions + return _sub_dict(self._joint_directions, name) + + def get_joint_offset_dict(self, + name: Tuple[Text] = None) -> Dict[Text, float]: + if name is None: + return self._joint_offsets + return _sub_dict(self._joint_offsets, name) + + def get_end_effector_id_dict(self, + name: Tuple[Text] = None) -> Dict[Text, int]: + if name is None: + return self._end_effector_dict + return _sub_dict(self._end_effector_dict, name) + + @property + def robot_id(self): + """Returns the unique object instance id of this loaded URDF in pybullet. + + Note this is different from all other get_{}_id APIs, which returns the + joint/link id within this robot instance. + + Returns: + The object id as returned by loadURDF. + """ + return self._robot_id + + @property + def all_joint_info(self): + return self._all_joint_info + + @property + def user_dict(self): + return self._user_dict + + @property + def motor_names(self): + return self._motor_names + + @property + def constrained_base(self): + return self._constrained_base + + def _build_base_dict(self): + """Builds the base joints dictionary. + + In pybullet, a link's id within the robot always equal to its parent joint + id. So this base joint dict functionaly is equivalent to the base link dict. + The dictionary may only contain {ROBOT_BASE: -1} if self._base_names is + empty. + + Returns: + The base link (joint) ordered dictionary. + """ + base_dict = collections.OrderedDict() + if self._base_names is None: + base_dict[ROBOT_BASE] = -1 + else: + base_dict.update(_sub_dict(self._joint_name_to_id, self._base_names)) + return base_dict + + def _build_user_dict(self): + """Builds a dictionary using user defined joint groups.""" + user_dict = collections.OrderedDict() + if self._user_group is None: + return user_dict + for group_name, group_joint_names in self._user_group.items(): + user_dict[group_name] = collections.OrderedDict() + user_dict[group_name].update( + _sub_dict(self._joint_name_to_id, group_joint_names)) + return user_dict + + def _build_all_joint_dict(self): + """Extracts all joints from the URDF. + + Finds all joints (fixed or movable) in the URDF and extracts the info. This + includes actuated joints (i.e. motors), and non-actuated joints, e.g. the + passive joints in Minitaur's four bar mechanism, and fixed joints connecting + the toe and the lower legs, etc. + + Returns: + number of joints, all joint information as returned by pybullet, and the + joint_name_to_id dictionary. + + """ + num_joints = self._pybullet_client.getNumJoints(self._robot_id) + all_joint_info = [ + self._pybullet_client.getJointInfo(self._robot_id, i) + for i in range(num_joints) + ] + + # Remove the default joint dampings to increase sim fidelity. + for joint_info in all_joint_info: + joint_id = joint_info[0] + self._pybullet_client.changeDynamics( + joint_id, -1, linearDamping=0, angularDamping=0) + + joint_name_to_id = collections.OrderedDict() + link_name_to_id = collections.OrderedDict([(ROBOT_BASE, -1)]) + for joint_info in all_joint_info: + joint_name = joint_info[1].decode("UTF-8") + joint_id = joint_info[0] + joint_name_to_id[joint_name] = joint_id + # Index 12 is the name of the joint's child link, and in PyBullet a child + # link id is always equal to its parent joint id. + link_name_to_id[joint_info[12].decode("UTF-8")] = joint_id + + return num_joints, all_joint_info, joint_name_to_id, link_name_to_id + + def load( + self, + enable_self_collision: bool = None, + init_base_position: Tuple[float] = None, + init_base_orientation_quaternion: Tuple[float] = None, + init_joint_angles: Dict[Text, float] = None, + ): + """Reloads the URDF and rebuilds the dictionaries.""" + if enable_self_collision is None: + enable_self_collision = self._enable_self_collision + if init_base_position is None: + init_base_position = self._init_base_position + if init_base_orientation_quaternion is None: + init_base_orientation_quaternion = self._init_base_orientation_quaternion + + self._robot_id = self._load_urdf(enable_self_collision, init_base_position, + init_base_orientation_quaternion) + + (self._num_joints, self._all_joint_info, self._joint_name_to_id, + self._link_name_to_id) = self._build_all_joint_dict() + self._base_dict = self._build_base_dict() + self._motor_dict = _sub_dict(self._joint_name_to_id, self._motor_names) + self._end_effector_dict = _sub_dict(self._joint_name_to_id, + self._end_effector_names) + self._user_dict = self._build_user_dict() + + self.reset_base_pose(init_base_position, init_base_orientation_quaternion) + self.reset_joint_angles(init_joint_angles) + + def _load_urdf(self, enable_self_collision: bool, + init_base_position: Tuple[float], + init_base_orientation_quaternion: Tuple[float]) -> int: + """Loads the URDF and returns the pybullet id.""" + try: + if enable_self_collision: + return self._pybullet_client.loadURDF( + self._urdf_path, + init_base_position, + init_base_orientation_quaternion, + useFixedBase=self._constrained_base, + flags=self._pybullet_client.URDF_USE_SELF_COLLISION) + else: + return self._pybullet_client.loadURDF( + self._urdf_path, + init_base_position, + init_base_orientation_quaternion, + useFixedBase=self._constrained_base, + ) + except: + print("!!!!!!!!!!!!!!!!") + print("Error: cannot find file:") + print(self._urdf_path) + import sys + sys.exit(0) + + def reset_joint_angles(self, joint_angles: Dict[Text, float] = None): + """Resets the joint angles. + + Resets the joint poses. This is instanteneously and will ignore the physics + (e.g. collisions, inertias, etc). Should only be used during the episode + reset time. Does not work for real robots (other than changing the + visualization). This API has no effect if both the input joint_angles and + the self._init_joint_angles are None. + + Args: + joint_angles: The joint angles in the robot's joint space. + + Raises: + AttributeError if the joint directions and joint offsets are not provided + during init. + """ + if self._init_joint_angles is None: + return + + if joint_angles is None: + joint_angles = self._init_joint_angles + + if self._joint_directions is None or self._joint_offsets is None: + raise AttributeError( + "joint directions and joint offsets not provided in __init__") + + for joint_name, angle in joint_angles.items(): + urdf_joint_angle = angle * self._joint_directions[ + joint_name] + self._joint_offsets[joint_name] + self._pybullet_client.resetJointState( + self._robot_id, + self._joint_name_to_id[joint_name], + urdf_joint_angle, + targetVelocity=0) + + def reset_base_pose( + self, + base_position: Tuple[float] = None, + base_orientation_quaternion: Tuple[float] = None, + ): + """Resets the base position and orientation. + + Instanteneously re-position the base pose of the robot. Does not work for + real robots except for the visualization. + + Args: + base_position: Base x, y, z position. + base_orientation_quaternion: Base rotation. + """ + if base_position is None: + base_position = self._init_base_position + if base_orientation_quaternion is None: + base_orientation_quaternion = self._init_base_orientation_quaternion + self._pybullet_client.resetBasePositionAndOrientation( + self._robot_id, base_position, base_orientation_quaternion) + self._pybullet_client.resetBaseVelocity(self._robot_id, (0, 0, 0), + (0, 0, 0)) + + @property + def com_dof(self): + return 0 if self._constrained_base else 6 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/data_types.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/data_types.py new file mode 100644 index 000000000..853bb567b --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/data_types.py @@ -0,0 +1,67 @@ +"""Definitions of safety layer data types.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import attr + + +@attr.s +class Bound(object): + """Struct for inclusive lower and upper bounds.""" + lower = attr.ib(type=float, default=0) + upper = attr.ib(type=float, default=0) + + @upper.validator # pytype: disable=attribute-error + def _upper_greator_equal_to_lower(self, attribute, value): + del attribute + assert value >= self.lower, ( + "upper bound {} is less than lower bound {}".format(value, self.lower)) + + +@attr.s +class SafetyConfig(object): + """Struct to configure the safety module.""" + motor_position_bound = attr.ib(type=list) + motor_position_gain_bound = attr.ib(type=list) + motor_velocity_bound = attr.ib(type=list) + motor_velocity_gain_bound = attr.ib(type=list) + motor_torque_bound = attr.ib(type=list) + timestamp_delta_bound = attr.ib(type=Bound) + motor_average_abs_velocity_bound = attr.ib(type=list) + motor_average_abs_power_bound = attr.ib(type=list) + state_action_timestamp_delta_bound = attr.ib(type=float) + motor_delta_position_bound = attr.ib(type=list) + motor_average_abs_delta_position_bound = attr.ib(type=list) + + +@attr.s +class MotorState(object): + """A generic type for motor state. + + Motor states are what we can potentially read from the motor encoder or + firmware APIs. + + """ + timestamp = attr.ib(type=float, default=None) + position = attr.ib(type=float, default=None) + position_gain = attr.ib(type=float, default=None) + velocity = attr.ib(type=float, default=None) + velocity_gain = attr.ib(type=float, default=None) + torque = attr.ib(type=float, default=None) + + +@attr.s +class MotorAction(object): + """A generic type for motor action. + + Motor actions are the potential command structure we can send to the motor. + While similar to MotorState, they are logically very different entities. + """ + timestamp = attr.ib(type=float, default=None) + position = attr.ib(type=float, default=None) + position_gain = attr.ib(type=float, default=None) + velocity = attr.ib(type=float, default=None) + velocity_gain = attr.ib(type=float, default=None) + torque = attr.ib(type=float, default=None) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_action_validator.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_action_validator.py new file mode 100644 index 000000000..567add414 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_action_validator.py @@ -0,0 +1,128 @@ +"""Validates the motor commands.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import typing +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots.safety import data_types +from pybullet_envs.minitaur.robots.safety import utilities +from pybullet_envs.minitaur.robots.safety.python import moving_window_filter + +_DEQUE_SIZE = 200 + + +class MotorActionValidator(object): + """A safety guard to check motor actions. + + Monitors the commands sent to the motor and detect unsafe behaviors. + """ + + def __init__( + self, + motor_id: typing.Any, + position_bound: data_types.Bound, + position_gain_bound: data_types.Bound, + velocity_bound: data_types.Bound, + velocity_gain_bound: data_types.Bound, + torque_bound: data_types.Bound, + timestamp_delta_bound: data_types.Bound, + delta_position_bound: data_types.Bound, + average_abs_delta_position_bound: data_types.Bound, + state_buffer_size: int = _DEQUE_SIZE, + ): + """Initializes the class. + + Args: + motor_id: Unique ID for the motor. + position_bound: The lower/upper bound of the motor angle. + position_gain_bound: The lower/upper bound of the motor position gain for + PD control. + velocity_bound: The lower/upper bound of the motor speed. + velocity_gain_bound: The lower/upper bound of the motor velocity gain for + PD control. + torque_bound: The lower/upper bound of the measured motor torque. + timestamp_delta_bound: The range of timestamp difference between two + consecutively received motor states. + delta_position_bound: The bound between the current motor position and the + command position, in position control mode. + average_abs_delta_position_bound: The bound for average motor position and + command poisition difference. + state_buffer_size: The buffer size used to calculate the average. + """ + assert state_buffer_size > 1 + self._last_motor_state = None + + self._motor_id = motor_id + self._position_bound = position_bound + self._position_gain_bound = position_gain_bound + self._velocity_bound = velocity_bound + self._velocity_gain_bound = velocity_gain_bound + self._torque_bound = torque_bound + self._timestamp_delta_bound = timestamp_delta_bound + self._delta_position_bound = delta_position_bound + self._average_abs_delta_position_bound = average_abs_delta_position_bound + self._abs_delta_position_filter = moving_window_filter.MovingWindowFilter( + state_buffer_size) + + def on_state(self, new_state: data_types.MotorState): + """Updates the last motor state. + + Args: + new_state: The latest motor state. + """ + self._last_motor_state = new_state + + def on_action(self, new_action: data_types.MotorAction, + control_mode: robot_config.MotorControlMode): + """Adds a new motor action and validates it. + + Args: + new_action: A new action that will be send to the motor. + control_mode: The motor control mode. + + Raises: + safety_error.OutOfBoundError: When any of the motor action fields or + state-action difference is out of bound. + """ + + # We first validate the new state. + + motor_str = "motor {} ".format(self._motor_id) + if (control_mode == robot_config.MotorControlMode.POSITION or + control_mode == robot_config.MotorControlMode.HYBRID): + utilities.assert_in_bound(motor_str + "position", new_action.position, + self._position_bound) + utilities.assert_in_bound(motor_str + "velocity", new_action.velocity, + self._velocity_bound) + utilities.assert_in_bound(motor_str + "position gain", + new_action.position_gain, + self._position_gain_bound) + utilities.assert_in_bound(motor_str + "velocity gain", + new_action.velocity_gain, + self._velocity_gain_bound) + + utilities.assert_in_bound(motor_str + "torque", new_action.torque, + self._torque_bound) + + if self._last_motor_state is None: + return + + delta_time = new_action.timestamp - self._last_motor_state.timestamp + utilities.assert_in_bound(motor_str + "state-action timestamp difference", + delta_time, self._timestamp_delta_bound) + + # To detect the bang-bang type controller behavior. + if (control_mode == robot_config.MotorControlMode.POSITION or + control_mode == robot_config.MotorControlMode.HYBRID): + delta_position = new_action.position - self._last_motor_state.position + utilities.assert_in_bound(motor_str + "state-action position difference", + delta_position, self._delta_position_bound) + + average_abs_delta_position = ( + self._abs_delta_position_filter.CalculateAverage( + abs(delta_position))) + utilities.assert_in_bound( + motor_str + "average state-action position difference", + average_abs_delta_position, self._average_abs_delta_position_bound) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_state_validator.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_state_validator.py new file mode 100644 index 000000000..3ccf34c02 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_state_validator.py @@ -0,0 +1,137 @@ +"""Software safety layer for robot control. + +Validates the motor states received from the motor encoder. + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import typing + +from pybullet_envs.minitaur.robots.safety import data_types +from pybullet_envs.minitaur.robots.safety import utilities +from pybullet_envs.minitaur.robots.safety.python import moving_window_filter + +# The default internal buffer size for the MotorStateValidator. +_DEQUE_SIZE = 200 + + +class MotorStateValidator(object): + """A safety guard to check motor states. + + Monitors the status of the motor and detects anomalies in the + readings. For example, the class will throw safety errors if the motor + velocity is too large. Currently we support checking of motor angle, velocity, + gain, torque, as well as the timestamp interval. + + Attributes: + last_state: The last received motor state. + """ + + def __init__( + self, + motor_id: typing.Any, + position_bound: data_types.Bound, + position_gain_bound: data_types.Bound, + velocity_bound: data_types.Bound, + velocity_gain_bound: data_types.Bound, + torque_bound: data_types.Bound, + timestamp_delta_bound: data_types.Bound, + average_abs_velocity_bound: data_types.Bound, + average_abs_power_bound: data_types.Bound, + state_buffer_size: int = _DEQUE_SIZE, + ): + """Initializes the class. + + Args: + motor_id: Unique ID for the motor. + position_bound: The lower/upper bound of the motor angle. + position_gain_bound: The lower/upper bound of the motor position gain for + PD control. + velocity_bound: The lower/upper bound of the motor speed. + velocity_gain_bound: The lower/upper bound of the motor velocity gain for + PD control. + torque_bound: The lower/upper bound of the measured motor torque. + timestamp_delta_bound: The range of timestamp difference between two + consecutively received motor states. + average_abs_velocity_bound: The average absolute velocity limit. + average_abs_power_bound: The average absolute mechanical power limit. + state_buffer_size: The buffer size used to calculate the average. + """ + assert state_buffer_size > 1 + self.last_state = None + + self._motor_id = motor_id + self._position_bound = position_bound + self._position_gain_bound = position_gain_bound + self._velocity_bound = velocity_bound + self._velocity_gain_bound = velocity_gain_bound + self._torque_bound = torque_bound + self._timestamp_delta_bound = timestamp_delta_bound + self._average_abs_velocity_bound = average_abs_velocity_bound + self._average_abs_power_bound = average_abs_power_bound + + # For velocity/power, we use a filter to compute their averages + # over a small period. This is to avoid the noisy readings giving false + # positive. + self._abs_velocity_filter = moving_window_filter.MovingWindowFilter( + state_buffer_size) + self._abs_power_filter = moving_window_filter.MovingWindowFilter( + state_buffer_size) + + def on_state(self, new_state: data_types.MotorState): + """Adds a new motor state and validates it. + + Will validate both the instantenous state as well as statitical + averages. + + Args: + new_state: A new state from the motor encoder. + + Raises: + safety_error.OutOfBoundError: When any of the motor readings (e.g. + position, torque) is out of bound. + """ + + # We first validate the new state. + + motor_str = "motor {} ".format(self._motor_id) + utilities.assert_in_bound(motor_str + "position", new_state.position, + self._position_bound) + utilities.assert_in_bound(motor_str + "velocity", new_state.velocity, + self._velocity_bound) + utilities.assert_in_bound(motor_str + "position gain", + new_state.position_gain, + self._position_gain_bound) + utilities.assert_in_bound(motor_str + "velocity gain", + new_state.velocity_gain, + self._velocity_gain_bound) + utilities.assert_in_bound(motor_str + "torque", new_state.torque, + self._torque_bound) + + if not self.last_state: + self.last_state = new_state + return + + last_state = self.last_state + + # Check if the time interval between two received states are large. + + delta_time = new_state.timestamp - last_state.timestamp + utilities.assert_in_bound(motor_str + "timestamp", delta_time, + self._timestamp_delta_bound) + + average_abs_velocity = self._abs_velocity_filter.CalculateAverage( + abs(new_state.velocity)) + utilities.assert_in_bound(motor_str + "average velocity", + average_abs_velocity, + self._average_abs_velocity_bound) + + average_abs_power = self._abs_power_filter.CalculateAverage( + abs(new_state.velocity * new_state.torque)) + utilities.assert_in_bound(motor_str + "average power", average_abs_power, + self._average_abs_power_bound) + + self.last_state = new_state diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/moving_window_filter.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/moving_window_filter.py new file mode 100644 index 000000000..4249677c5 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/moving_window_filter.py @@ -0,0 +1,68 @@ +"""Moving window filter to smooth out sensor readings.""" + +import collections + +class MovingWindowFilter(object): + """A stable O(1) moving filter for incoming data streams. + + We implement the Neumaier's algorithm to calculate the moving window average, + which is numerically stable. + + """ + + def __init__(self, window_size: int): + """Initializes the class. + + Args: + window_size: The moving window size. + """ + assert window_size > 0 + self._window_size = window_size + self._value_deque = collections.deque(maxlen=window_size) + # The moving window sum. + self._sum = 0 + # The correction term to compensate numerical precision loss during + # calculation. + self._correction = 0 + + def _neumaier_sum(self, value: float): + """Update the moving window sum using Neumaier's algorithm. + + For more details please refer to: + https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Further_enhancements + + Args: + value: The new value to be added to the window. + """ + + new_sum = self._sum + value + if abs(self._sum) >= abs(value): + # If self._sum is bigger, low-order digits of value are lost. + self._correction += (self._sum - new_sum) + value + else: + # low-order digits of sum are lost + self._correction += (value - new_sum) + self._sum + + self._sum = new_sum + + def calculate_average(self, new_value: float) -> float: + """Computes the moving window average in O(1) time. + + Args: + new_value: The new value to enter the moving window. + + Returns: + The average of the values in the window. + + """ + deque_len = len(self._value_deque) + if deque_len < self._value_deque.maxlen: + pass + else: + # The left most value to be subtracted from the moving sum. + self._neumaier_sum(-self._value_deque[0]) + + self._neumaier_sum(new_value) + self._value_deque.append(new_value) + + return (self._sum + self._correction) / self._window_size diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_checker.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_checker.py new file mode 100644 index 000000000..765028c0e --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_checker.py @@ -0,0 +1,119 @@ +"""The generic safety checking interface. + +Defines the generic safety checker class that can detect bad motor states, imu +states, self-collisions, unsafe motor commands, unusual temperature reading, +etc. Safety criterions are provided by the robot class. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import typing +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots.safety import data_types +from pybullet_envs.minitaur.robots.safety import motor_action_validator +from pybullet_envs.minitaur.robots.safety import motor_state_validator +from pybullet_envs.minitaur.robots.safety import utilities + +_MOTOR_STATE_BUFFER_SIZE = 500 + + +class SafetyChecker(object): + """The generic safety checking interface.""" + + def __init__( + self, + robot: typing.Any, + ): + """Initilaizes the class. + + TODO(b/131377892): Implement other state checkings including the + IMU/temperature/contact force if enabled. + + Args: + robot: A robot instance such like Minitaur/Laikago/Vision60. + """ + self._robot = robot + + self._motor_state_validators = [] + self._motor_action_validators = [] + for i in range(robot.num_motors): + self._motor_state_validators.append( + motor_state_validator.MotorStateValidator( + motor_id=i, + position_bound=robot.safety_config.motor_position_bound[i], + position_gain_bound=robot.safety_config + .motor_position_gain_bound[i], + velocity_bound=robot.safety_config.motor_velocity_bound[i], + velocity_gain_bound=robot.safety_config + .motor_velocity_gain_bound[i], + torque_bound=robot.safety_config.motor_torque_bound[i], + timestamp_delta_bound=robot.safety_config.timestamp_delta_bound, + average_abs_velocity_bound=robot.safety_config + .motor_average_abs_velocity_bound[i], + average_abs_power_bound=robot.safety_config + .motor_average_abs_power_bound[i], + state_buffer_size=_MOTOR_STATE_BUFFER_SIZE, + )) + self._motor_action_validators.append( + motor_action_validator.MotorActionValidator( + motor_id=i, + position_bound=robot.safety_config.motor_position_bound[i], + position_gain_bound=robot.safety_config + .motor_position_gain_bound[i], + velocity_bound=robot.safety_config.motor_velocity_bound[i], + velocity_gain_bound=robot.safety_config + .motor_velocity_gain_bound[i], + torque_bound=robot.safety_config.motor_torque_bound[i], + timestamp_delta_bound=robot.safety_config + .state_action_timestamp_delta_bound, + delta_position_bound=robot.safety_config + .motor_delta_position_bound[i], + average_abs_delta_position_bound=robot.safety_config + .motor_average_abs_delta_position_bound[i], + state_buffer_size=_MOTOR_STATE_BUFFER_SIZE, + )) + + def check_state(self) -> None: + """Validates the state of the robot. + + TODO(b/131377892): Implement other state checkings including the + IMU/temperature/contact force if enabled. + + Raises: + A safety exception if any state checking (motor/imu/etc) fails. + """ + + for motor_id, state_validator, action_validator in zip( + range(self._robot.num_motors), self._motor_state_validators, + self._motor_action_validators): + motor_state = data_types.MotorState( + timestamp=self._robot.last_state_time, + position=self._robot.GetMotorAngles()[motor_id], + velocity=self._robot.GetMotorVelocities()[motor_id], + position_gain=self._robot.GetMotorPositionGains()[motor_id], + velocity_gain=self._robot.GetMotorVelocityGains()[motor_id], + torque=self._robot.GetMotorTorques()[motor_id], + ) + state_validator.on_state(motor_state) + action_validator.on_state(motor_state) + + def check_motor_action( + self, + action: typing.Sequence[float], + control_mode: robot_config.MotorControlMode, + ) -> None: + """Validate the action w.r.t to the motor states. + + Args: + action: The motor commands sent to the robot. + control_mode: The motor control mode. + + Raises: + A safety exception if action checking fails. + """ + motor_action_list = utilities.convert_to_motor_action( + self._robot, action, control_mode) + for motor_id, validator in enumerate(self._motor_action_validators): + validator.on_action(motor_action_list[motor_id], control_mode) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_error.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_error.py new file mode 100644 index 000000000..5ff078a6f --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/safety_error.py @@ -0,0 +1,11 @@ +"""Exception types for safety related error.""" + + +class SafetyError(Exception): + """The base safety exception.""" + pass + + +class OutOfBoundError(SafetyError): + """Rasied when values like motor position or velocity is out of bound.""" + pass diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/utilities.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/utilities.py new file mode 100644 index 000000000..9703b6bf0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/utilities.py @@ -0,0 +1,163 @@ +"""Utilities for safety layers.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import typing +from pybullet_envs.minitaur.robots import robot_config +from pybullet_envs.minitaur.robots.safety import data_types +from pybullet_envs.minitaur.robots.safety import safety_error + + +def assert_in_bound(name: typing.Text, value: float, bound: data_types.Bound): + """Check if the given value is within the provided bound. + + Args: + name: The name of the value. + value: Number to be checked. + bound: Contains the lower and upper bounds. The bound is inclusive. + + Raises: + safety_error.OutofBoundError: when the value is outside the bound. + """ + if bound.lower <= value <= bound.upper: + return + else: + raise safety_error.OutOfBoundError("{} is out of bound {} for {}".format( + value, bound, name)) + + +def convert_to_motor_action( + robot: typing.Any, + action: typing.Sequence[float], + control_mode: robot_config.MotorControlMode, +): + """Converts the input action to generic MotorAction classes. + + Args: + robot: An robot instance. + action: The motor commands sent to the robot. + control_mode: The motor control mode. + + Returns: + The list of converted MotorAction instances. + """ + motor_action_list = [] + if control_mode == robot_config.MotorControlMode.POSITION: + for motor_id, position in enumerate(action): + motor_action_list.append( + data_types.MotorAction( + timestamp=robot.last_action_time, + position=position, + position_gain=robot.GetMotorPositionGains()[motor_id], + velocity=0, + velocity_gain=robot.GetMotorVelocityGains()[motor_id], + torque=0)) + + if (control_mode == robot_config.MotorControlMode.TORQUE or + control_mode == robot_config.MotorControlMode.PWM): + for motor_id, torque in enumerate(action): + motor_action_list.append( + data_types.MotorAction( + timestamp=robot.last_action_time, + position=0, + position_gain=0, + velocity=0, + velocity_gain=0, + torque=torque)) + + if control_mode == robot_config.MotorControlMode.HYBRID: + for motor_id in range(robot.num_motors): + position_index = ( + motor_id * robot_config.HYBRID_ACTION_DIMENSION + + robot_config.HybridActionIndex.POSITION.value) + position_gain_index = ( + motor_id * robot_config.HYBRID_ACTION_DIMENSION + + robot_config.HybridActionIndex.POSITION_GAIN.value) + velocity_index = ( + motor_id * robot_config.HYBRID_ACTION_DIMENSION + + robot_config.HybridActionIndex.VELOCITY.value) + velocity_gain_index = ( + motor_id * robot_config.HYBRID_ACTION_DIMENSION + + robot_config.HybridActionIndex.VELOCITY_GAIN.value) + torque_index = ( + motor_id * robot_config.HYBRID_ACTION_DIMENSION + + robot_config.HybridActionIndex.TORQUE.value) + motor_action_list.append( + data_types.MotorAction( + timestamp=robot.last_action_time, + position=action[position_index], + position_gain=action[position_gain_index], + velocity=action[velocity_index], + velocity_gain=action[velocity_gain_index], + torque=action[torque_index])) + + return motor_action_list + + +class MovingWindowFilter(object): + """A stable O(1) moving filter for incoming data streams. + + We implement the Neumaier's algorithm to calculate the moving window average, + which is numerically stable. + + """ + + def __init__(self, window_size: int): + """Initializes the class. + + Args: + window_size: The moving window size. + """ + assert window_size > 0 + self._window_size = window_size + self._value_deque = collections.deque(maxlen=window_size) + # The moving window sum. + self._sum = 0 + # The correction term to compensate numerical precision loss during + # calculation. + self._correction = 0 + + def _neumaier_sum(self, value: float): + """Update the moving window sum using Neumaier's algorithm. + + For more details please refer to: + https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Further_enhancements + + Args: + value: The new value to be added to the window. + """ + + new_sum = self._sum + value + if abs(self._sum) >= abs(value): + # If self._sum is bigger, low-order digits of value are lost. + self._correction += (self._sum - new_sum) + value + else: + # low-order digits of sum are lost + self._correction += (value - new_sum) + self._sum + + self._sum = new_sum + + def calculate_average(self, new_value: float) -> float: + """Computes the moving window average in O(1) time. + + Args: + new_value: The new value to enter the moving window. + + Returns: + The average of the values in the window. + + """ + deque_len = len(self._value_deque) + if deque_len < self._value_deque.maxlen: + pass + else: + # The left most value to be subtracted from the moving sum. + self._neumaier_sum(-self._value_deque[0]) + + self._neumaier_sum(new_value) + self._value_deque.append(new_value) + + return (self._sum + self._correction) / self._window_size diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/time_ordered_buffer.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/time_ordered_buffer.py new file mode 100644 index 000000000..52cf6b86d --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/time_ordered_buffer.py @@ -0,0 +1,263 @@ +# Lint as: python3 +"""The common class to manage the a stream of past data.""" + +import collections +from typing import Any, List, Sequence, Union +import dataclasses +import gin +import numpy as np + + +@dataclasses.dataclass +class BufferTuple: + value_0: Any + value_1: Any + coeff: float + + +FloatOrArray = Union[float, Sequence[float]] +BufferTupleOrArray = Union[BufferTuple, Sequence[BufferTuple]] +TIME_IDX = 0 +VALUE_IDX = 1 + + +@gin.configurable +class TimeOrderedBuffer(object): + """A buffer to hold and extract history data.""" + + def __init__(self, + max_buffer_timespan: float, + error_on_timestamp_reversal: bool = True, + error_on_duplicate_timestamp: bool = True, + replace_value_on_duplicate_timestamp: bool = False, + ): + """Initializes the class. + + Args: + max_buffer_timespan: Maximum amount of buffer by time to keep. + error_on_timestamp_reversal: Whether to throw error if inverted timestamps + are found. + error_on_duplicate_timestamp: Whether to throw error if the incoming + data has the same timestamp as the latest timestamp in the buffer. + replace_value_on_duplicate_timestamp: Whether to keep the new value when + a duplicate timestamp has occurred. This only applies if we are not + throwing an error on duplicate timestamps. + """ + if max_buffer_timespan < 0: + raise ValueError( + "Invalid max_buffer_timespan: {}".format(max_buffer_timespan)) + self._max_buffer_timespan = max_buffer_timespan + self._error_on_timestamp_reversal = error_on_timestamp_reversal + self._error_on_duplicate_timestamp = error_on_duplicate_timestamp + self._replace_value_on_duplicate_timestamp = ( + replace_value_on_duplicate_timestamp) + # TODO(tsangwei): Look for a ring buffer implementation. + self._buffer = collections.deque() + + def reset(self): + self._buffer.clear() + + def _compute_coeff(self, + newer_time: float, + older_time: float, + target_time: float, + ) -> float: + """Compute the coefficient value between the two timestamps. + + Args: + newer_time: The newer timestamp. + older_time: The older timestamp. + target_time: Target timestamp that is between the newer and older values. + + Returns: + The coefficient as a float. + """ + coeff = 0.0 + # Prevents divide by 0 error. + if newer_time != older_time: + assert older_time <= target_time <= newer_time + coeff = (newer_time - target_time) / (newer_time - older_time) + return coeff + + def _pack_data(self, + obs_newer: Any, + obs_older: Any, + target_time: float, + ) -> BufferTuple: + """Packs up buffer data as BufferTuple dataclass. + + Args: + obs_newer: Timestamp and value of newer observation. + obs_older: Timestamp and value of older observation. + target_time: Target timestamp of the observation we are looking for. + + Returns: + BufferTuple dataclass. + """ + coeff = self._compute_coeff(newer_time=obs_newer[TIME_IDX], + older_time=obs_older[TIME_IDX], + target_time=target_time) + return BufferTuple(value_0=obs_newer[VALUE_IDX], + value_1=obs_older[VALUE_IDX], + coeff=coeff) + + def _find_values_at(self, timestamp_targets: Sequence[float]) -> List[Any]: + """Get the lower/upper bound values for given target timestamp. + + Args: + timestamp_targets: Actual timestamp value to match against. + + Returns: + List of BufferTuple dataclass. + """ + results = [None] * len(timestamp_targets) + oldest_obs = self._buffer[0] + latest_obs = self._buffer[-1] + + search_start_idx = None + search_end_idx = None + + # Check to make sure we do not try to search for values outside of the + # current buffer. + for i in range(len(timestamp_targets)): + # Oldest observation have the smallest timestamp. + if timestamp_targets[i] <= oldest_obs[TIME_IDX]: + results[i] = self._pack_data(obs_newer=oldest_obs, + obs_older=oldest_obs, + target_time=timestamp_targets[i]) + elif timestamp_targets[i] >= latest_obs[TIME_IDX]: + results[i] = self._pack_data(obs_newer=latest_obs, + obs_older=latest_obs, + target_time=timestamp_targets[i]) + else: + if search_end_idx is None: + search_end_idx = i + search_start_idx = i + + if search_end_idx is not None: + results = self._walkthrough_buffer(timestamp_targets=timestamp_targets, + search_start_idx=search_start_idx, + search_end_idx=search_end_idx, + results=results) + + return results + + def _walkthrough_buffer(self, + timestamp_targets: List[float], + search_start_idx: int, + search_end_idx: int, + results: List[BufferTuple], + ) -> List[BufferTuple]: + """Actual method to walk through the buffer looking for requested values. + + Args: + timestamp_targets: List of timestamps to search for in buffer. + search_start_idx: Index number for timestamp_targets to start searching + from. + search_end_idx: Index number for timestamp_targets to stop searching at. + results: List of BufferTuple values that covers out of bound results. + + Returns: + List of BufferTuple values. + """ + value_older = None + target_idx = search_start_idx + target_timestamp = timestamp_targets[target_idx] + value_older = self._buffer[0] + + # Searching from oldest timestamp to latest timestamp. + for value_newer in self._buffer: + # Catch edge case scenario where multiple timestamp targets are between + # the same two buffer timestamps. (b/157104935) + while value_newer[TIME_IDX] >= target_timestamp: + # Catch special edge case scenario if using older_obs_blender method. + obs_older = value_newer if ( + value_newer[TIME_IDX] == target_timestamp) else value_older + results[target_idx] = self._pack_data(obs_newer=value_newer, + obs_older=obs_older, + target_time=target_timestamp) + if target_idx - 1 >= search_end_idx: + target_idx -= 1 + target_timestamp = timestamp_targets[target_idx] + else: + return results + value_older = value_newer + + return results + + def add(self, timestamp: float, value: Any): + """Inserts timestamp and value into buffer. + + Args: + timestamp: Timestamp of the data value. + value: Data value to be saved into the buffer. + """ + if self._buffer: + last_timestamp = self._buffer[-1][TIME_IDX] + if last_timestamp == timestamp: + if (not np.array_equal(self._buffer[-1][VALUE_IDX], value) and + self._error_on_duplicate_timestamp): + raise ValueError("Duplicate timestamp detected: {}".format(timestamp)) + else: + # Duplicate message detected. + if self._replace_value_on_duplicate_timestamp: + self._buffer[-1] = (timestamp, value) + return + if last_timestamp > timestamp and self._error_on_timestamp_reversal: + raise ValueError( + "Time reversal detected: new timestamp is {} vs last timestamp {}" + .format(timestamp, last_timestamp)) + # Dropping old buffer data that exceed buffer timespan limit and making + # sure the buffer does not go empty. + while (len(self._buffer) > 1 and self._max_buffer_timespan < + (timestamp - self._buffer[1][TIME_IDX])): + self._buffer.popleft() + self._buffer.append((timestamp, value)) + + def get_delayed_value(self, latency: FloatOrArray) -> BufferTupleOrArray: + """Retrieves value in the history buffer according to latency. + + Finds the closest pair of values that are some time (i.e. latency) ago from + the most recent timestamp. Suppose the history buffer looks like this: + + [(0, val_x),...,(0.6, val_k-1), (0.7, val_k), (0.8, val_k+1),...,(2, val_0)] + + And the latency is '1.33', then this API will locate the values with + timestamps close to 2 - 1.33 = 0.67. So in this case, it will return the + pair (0.7, val_k) and (0.6, val_k+1), as well as a blending coefficient + which is calculated by (0.7 - 0.67) / (0.7 - 0.6) = 0.3. This blending coeff + can be used to linearly interpolate the returned values, i.e. val_1 * (1 - + coeff) + val_2 * coeff, if the multiply operator is defined. + + Args: + latency: The time interval(s) to look backwards in the history buffer from + the most recent timestamp. + + Returns: + An array of BufferTuple dataclass. + + Raises: + ValueError: if the latency is negative. + BufferError: if the buffer is empty. + """ + buffer_len = len(self._buffer) + if buffer_len == 0: + raise BufferError("The buffer is empty. Have you called 'add'?") + + single_latency = isinstance(latency, (int, float)) + + if single_latency: + if latency < 0: + raise ValueError("Latency cannot be negative.") + else: + if any(value < 0 for value in latency): + raise ValueError("Latency list contains negative values.") + if latency != sorted(latency): + raise ValueError("Invalid unsorted latency list.") + + target_list = [latency] if single_latency else latency + current_time = self._buffer[-1][TIME_IDX] + target_list = [current_time - offset for offset in target_list] + + result = self._find_values_at(target_list) + return result[0] if single_latency else result diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp.proto b/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp.proto new file mode 100644 index 000000000..9c4c75cc3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp.proto @@ -0,0 +1,147 @@ +// Protocol Buffers - Google's data interchange format +// Copyright 2008 Google Inc. All rights reserved. +// https://developers.google.com/protocol-buffers/ +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +syntax = "proto3"; + +package google.protobuf; + +option csharp_namespace = "Google.Protobuf.WellKnownTypes"; +option cc_enable_arenas = true; +option go_package = "google.golang.org/protobuf/types/known/timestamppb"; +option java_package = "com.google.protobuf"; +option java_outer_classname = "TimestampProto"; +option java_multiple_files = true; +option objc_class_prefix = "GPB"; + +// A Timestamp represents a point in time independent of any time zone or local +// calendar, encoded as a count of seconds and fractions of seconds at +// nanosecond resolution. The count is relative to an epoch at UTC midnight on +// January 1, 1970, in the proleptic Gregorian calendar which extends the +// Gregorian calendar backwards to year one. +// +// All minutes are 60 seconds long. Leap seconds are "smeared" so that no leap +// second table is needed for interpretation, using a [24-hour linear +// smear](https://developers.google.com/time/smear). +// +// The range is from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59.999999999Z. By +// restricting to that range, we ensure that we can convert to and from [RFC +// 3339](https://www.ietf.org/rfc/rfc3339.txt) date strings. +// +// # Examples +// +// Example 1: Compute Timestamp from POSIX `time()`. +// +// Timestamp timestamp; +// timestamp.set_seconds(time(NULL)); +// timestamp.set_nanos(0); +// +// Example 2: Compute Timestamp from POSIX `gettimeofday()`. +// +// struct timeval tv; +// gettimeofday(&tv, NULL); +// +// Timestamp timestamp; +// timestamp.set_seconds(tv.tv_sec); +// timestamp.set_nanos(tv.tv_usec * 1000); +// +// Example 3: Compute Timestamp from Win32 `GetSystemTimeAsFileTime()`. +// +// FILETIME ft; +// GetSystemTimeAsFileTime(&ft); +// UINT64 ticks = (((UINT64)ft.dwHighDateTime) << 32) | ft.dwLowDateTime; +// +// // A Windows tick is 100 nanoseconds. Windows epoch 1601-01-01T00:00:00Z +// // is 11644473600 seconds before Unix epoch 1970-01-01T00:00:00Z. +// Timestamp timestamp; +// timestamp.set_seconds((INT64) ((ticks / 10000000) - 11644473600LL)); +// timestamp.set_nanos((INT32) ((ticks % 10000000) * 100)); +// +// Example 4: Compute Timestamp from Java `System.currentTimeMillis()`. +// +// long millis = System.currentTimeMillis(); +// +// Timestamp timestamp = Timestamp.newBuilder().setSeconds(millis / 1000) +// .setNanos((int) ((millis % 1000) * 1000000)).build(); +// +// +// Example 5: Compute Timestamp from Java `Instant.now()`. +// +// Instant now = Instant.now(); +// +// Timestamp timestamp = +// Timestamp.newBuilder().setSeconds(now.getEpochSecond()) +// .setNanos(now.getNano()).build(); +// +// +// Example 6: Compute Timestamp from current time in Python. +// +// timestamp = Timestamp() +// timestamp.GetCurrentTime() +// +// # JSON Mapping +// +// In JSON format, the Timestamp type is encoded as a string in the +// [RFC 3339](https://www.ietf.org/rfc/rfc3339.txt) format. That is, the +// format is "{year}-{month}-{day}T{hour}:{min}:{sec}[.{frac_sec}]Z" +// where {year} is always expressed using four digits while {month}, {day}, +// {hour}, {min}, and {sec} are zero-padded to two digits each. The fractional +// seconds, which can go up to 9 digits (i.e. up to 1 nanosecond resolution), +// are optional. The "Z" suffix indicates the timezone ("UTC"); the timezone +// is required. A proto3 JSON serializer should always use UTC (as indicated by +// "Z") when printing the Timestamp type and a proto3 JSON parser should be +// able to accept both UTC and other timezones (as indicated by an offset). +// +// For example, "2017-01-15T01:30:15.01Z" encodes 15.01 seconds past +// 01:30 UTC on January 15, 2017. +// +// In JavaScript, one can convert a Date object to this format using the +// standard +// [toISOString()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Date/toISOString) +// method. In Python, a standard `datetime.datetime` object can be converted +// to this format using +// [`strftime`](https://docs.python.org/2/library/time.html#time.strftime) with +// the time format spec '%Y-%m-%dT%H:%M:%S.%fZ'. Likewise, in Java, one can use +// the Joda Time's [`ISODateTimeFormat.dateTime()`]( +// http://www.joda.org/joda-time/apidocs/org/joda/time/format/ISODateTimeFormat.html#dateTime%2D%2D +// ) to obtain a formatter capable of generating timestamps in this format. +// +// +message Timestamp { + // Represents seconds of UTC time since Unix epoch + // 1970-01-01T00:00:00Z. Must be from 0001-01-01T00:00:00Z to + // 9999-12-31T23:59:59Z inclusive. + int64 seconds = 1; + + // Non-negative fractions of a second at nanosecond resolution. Negative + // second values with fractions must still have non-negative nanos values + // that count forward in time. Must be from 0 to 999,999,999 + // inclusive. + int32 nanos = 2; +} \ No newline at end of file diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp_pb2.py new file mode 100644 index 000000000..b14188991 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/timestamp_pb2.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: timestamp.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor.FileDescriptor( + name='timestamp.proto', + package='google.protobuf', + syntax='proto3', + serialized_options=b'\n\023com.google.protobufB\016TimestampProtoP\001Z2google.golang.org/protobuf/types/known/timestamppb\370\001\001\242\002\003GPB\252\002\036Google.Protobuf.WellKnownTypes', + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\x0ftimestamp.proto\x12\x0fgoogle.protobuf\"+\n\tTimestamp\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x12\r\n\x05nanos\x18\x02 \x01(\x05\x42\x85\x01\n\x13\x63om.google.protobufB\x0eTimestampProtoP\x01Z2google.golang.org/protobuf/types/known/timestamppb\xf8\x01\x01\xa2\x02\x03GPB\xaa\x02\x1eGoogle.Protobuf.WellKnownTypesb\x06proto3' +) + + + + +_TIMESTAMP = _descriptor.Descriptor( + name='Timestamp', + full_name='google.protobuf.Timestamp', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='seconds', full_name='google.protobuf.Timestamp.seconds', index=0, + number=1, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='nanos', full_name='google.protobuf.Timestamp.nanos', index=1, + number=2, type=5, cpp_type=1, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=36, + serialized_end=79, +) + +DESCRIPTOR.message_types_by_name['Timestamp'] = _TIMESTAMP +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +Timestamp = _reflection.GeneratedProtocolMessageType('Timestamp', (_message.Message,), { + 'DESCRIPTOR' : _TIMESTAMP, + '__module__' : 'timestamp_pb2' + # @@protoc_insertion_point(class_scope:google.protobuf.Timestamp) + }) +_sym_db.RegisterMessage(Timestamp) + + +DESCRIPTOR._options = None +# @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/action_filter.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/action_filter.py new file mode 100644 index 000000000..6fb100cb3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/action_filter.py @@ -0,0 +1,239 @@ +"""Two types of filters which can be applied to policy output sequences. + +1. Simple exponential filter +2. Butterworth filter - lowpass or bandpass + +The implementation of the butterworth filter follows scipy's lfilter +https://github.com/scipy/scipy/blob/v1.2.1/scipy/signal/signaltools.py + +We re-implement the logic in order to explicitly manage the y states + +The filter implements:: + a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M] + - a[1]*y[n-1] - ... - a[N]*y[n-N] + +We assume M == N. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +from absl import logging +import gin +import numpy as np +from scipy.signal import butter + +ACTION_FILTER_ORDER = 2 +ACTION_FILTER_LOW_CUT = 0.0 +ACTION_FILTER_HIGH_CUT = 4.0 + + +@gin.configurable +class ActionFilter(object): + """Implements a generic lowpass or bandpass action filter.""" + + def __init__(self, a, b, order, num_joints, ftype='lowpass'): + """Initializes filter. + + Either one per joint or same for all joints. + + Args: + a: filter output history coefficients + b: filter input coefficients + order: filter order + num_joints: robot DOF + ftype: filter type. 'lowpass' or 'bandpass' + """ + self.num_joints = num_joints + if isinstance(a, list): + self.a = a + self.b = b + else: + self.a = [a] + self.b = [b] + + # Either a set of parameters per joint must be specified as a list + # Or one filter is applied to every joint + if not ((len(self.a) == len(self.b) == num_joints) or ( + len(self.a) == len(self.b) == 1)): + raise ValueError('Incorrect number of filter values specified') + + # Normalize by a[0] + for i in range(len(self.a)): + self.b[i] /= self.a[i][0] + self.a[i] /= self.a[i][0] + + # Convert single filter to same format as filter per joint + if len(self.a) == 1: + self.a *= num_joints + self.b *= num_joints + self.a = np.stack(self.a) + self.b = np.stack(self.b) + + if ftype == 'bandpass': + assert len(self.b[0]) == len(self.a[0]) == 2 * order + 1 + self.hist_len = 2 * order + elif ftype == 'lowpass': + assert len(self.b[0]) == len(self.a[0]) == order + 1 + self.hist_len = order + else: + raise ValueError('%s filter type not supported' % (ftype)) + + logging.info('Filter shapes: a: %s, b: %s', self.a.shape, self.b.shape) + logging.info('Filter type:%s', ftype) + + self.yhist = collections.deque(maxlen=self.hist_len) + self.xhist = collections.deque(maxlen=self.hist_len) + self.reset() + + def reset(self): + """Resets the history buffers to 0.""" + self.yhist.clear() + self.xhist.clear() + for _ in range(self.hist_len): + self.yhist.appendleft(np.zeros((self.num_joints, 1))) + self.xhist.appendleft(np.zeros((self.num_joints, 1))) + + def filter(self, x): + """Returns filtered x.""" + xs = np.concatenate(list(self.xhist), axis=-1) + ys = np.concatenate(list(self.yhist), axis=-1) + y = np.multiply(x, self.b[:, 0]) + np.sum( + np.multiply(xs, self.b[:, 1:]), axis=-1) - np.sum( + np.multiply(ys, self.a[:, 1:]), axis=-1) + self.xhist.appendleft(x.reshape((self.num_joints, 1)).copy()) + self.yhist.appendleft(y.reshape((self.num_joints, 1)).copy()) + return y + + def init_history(self, x): + x = np.expand_dims(x, axis=-1) + for i in range(self.hist_len): + self.xhist[i] = x + self.yhist[i] = x + return + + +@gin.configurable +class ActionFilterButter(ActionFilter): + """Butterworth filter.""" + + def __init__(self, + lowcut=None, + highcut=None, + sampling_rate=None, + order=ACTION_FILTER_ORDER, + num_joints=None): + """Initializes a butterworth filter. + + Either one per joint or same for all joints. + + Args: + lowcut: list of strings defining the low cutoff frequencies. + The list must contain either 1 element (same filter for all joints) + or num_joints elements + 0 for lowpass, > 0 for bandpass. Either all values must be 0 + or all > 0 + highcut: list of strings defining the high cutoff frequencies. + The list must contain either 1 element (same filter for all joints) + or num_joints elements + All must be > 0 + sampling_rate: frequency of samples in Hz + order: filter order + num_joints: robot DOF + """ + self.lowcut = ([float(x) for x in lowcut] + if lowcut is not None else [ACTION_FILTER_LOW_CUT]) + self.highcut = ([float(x) for x in highcut] + if highcut is not None else [ACTION_FILTER_HIGH_CUT]) + if len(self.lowcut) != len(self.highcut): + raise ValueError('Number of lowcut and highcut filter values should ' + 'be the same') + + if sampling_rate is None: + raise ValueError('sampling_rate should be provided.') + + if num_joints is None: + raise ValueError('num_joints should be provided.') + + if np.any(self.lowcut): + if not np.all(self.lowcut): + raise ValueError('All the filters must be of the same type: ' + 'lowpass or bandpass') + self.ftype = 'bandpass' + else: + self.ftype = 'lowpass' + + a_coeffs = [] + b_coeffs = [] + for i, (l, h) in enumerate(zip(self.lowcut, self.highcut)): + if h <= 0.0: + raise ValueError('Highcut must be > 0') + + b, a = self.butter_filter(l, h, sampling_rate, order) + logging.info( + 'Butterworth filter: joint: %d, lowcut: %f, highcut: %f, ' + 'sampling rate: %d, order: %d, num joints: %d', i, l, h, + sampling_rate, order, num_joints) + b_coeffs.append(b) + a_coeffs.append(a) + + super(ActionFilterButter, self).__init__( + a_coeffs, b_coeffs, order, num_joints, self.ftype) + + def butter_filter(self, lowcut, highcut, fs, order=5): + """Returns the coefficients of a butterworth filter. + + If lowcut = 0, the function returns the coefficients of a low pass filter. + Otherwise, the coefficients of a band pass filter are returned. + Highcut should be > 0 + + Args: + lowcut: low cutoff frequency + highcut: high cutoff frequency + fs: sampling rate + order: filter order + Return: + b, a: parameters of a butterworth filter + """ + nyq = 0.5 * fs + low = lowcut / nyq + high = highcut / nyq + if low: + b, a = butter(order, [low, high], btype='band') + else: + b, a = butter(order, [high], btype='low') + return b, a + + +class ActionFilterExp(ActionFilter): + """Filter by way of simple exponential smoothing. + + y = alpha * x + (1 - alpha) * previous_y + """ + + def __init__(self, alpha, num_joints): + """Initialize the filter. + + Args: + alpha: list of strings defining the alphas. + The list must contain either 1 element (same filter for all joints) + or num_joints elements + 0 < alpha <= 1 + num_joints: robot DOF + """ + self.alphas = [float(x) for x in alpha] + logging.info('Exponential filter: alpha: %d', self.alphas) + + a_coeffs = [] + b_coeffs = [] + for a in self.alphas: + a_coeffs.append(np.asarray([1., a - 1.])) + b_coeffs.append(np.asarray([a, 0])) + + order = 1 + self.ftype = 'lowpass' + + super(ActionFilterExp, self).__init__( + a_coeffs, b_coeffs, order, num_joints, self.ftype) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics.py new file mode 100644 index 000000000..be5824400 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics.py @@ -0,0 +1,127 @@ +"""The inverse kinematic utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import typing + +_IDENTITY_ORIENTATION = (0, 0, 0, 1) + + +def joint_angles_from_link_position( + robot: typing.Any, + link_position: typing.Sequence[float], + link_id: int, + joint_ids: typing.Sequence[int], + position_in_world_frame=False, + base_translation: typing.Sequence[float] = (0, 0, 0), + base_rotation: typing.Sequence[float] = (0, 0, 0, 1)): + """Uses Inverse Kinematics to calculate joint angles. + + Args: + robot: A robot instance. + link_position: The (x, y, z) of the link in the body or the world frame, + depending on whether the argument position_in_world_frame is true. + link_id: The link id as returned from loadURDF. + joint_ids: The positional index of the joints. This can be different from + the joint unique ids. + position_in_world_frame: Whether the input link_position is specified + in the world frame or the robot's base frame. + base_translation: Additional base translation. + base_rotation: Additional base rotation. + + Returns: + A list of joint angles. + """ + if not position_in_world_frame: + # Projects to local frame. + base_position, base_orientation = robot.GetBasePosition( + ), robot.GetBaseOrientation() + base_position, base_orientation = robot.pybullet_client.multiplyTransforms( + base_position, base_orientation, base_translation, base_rotation) + + # Projects to world space. + world_link_pos, _ = robot.pybullet_client.multiplyTransforms( + base_position, base_orientation, link_position, _IDENTITY_ORIENTATION) + else: + world_link_pos = link_position + + ik_solver = 0 + all_joint_angles = robot.pybullet_client.calculateInverseKinematics( + robot.quadruped, link_id, world_link_pos, solver=ik_solver) + + # Extract the relevant joint angles. + joint_angles = [all_joint_angles[i] for i in joint_ids] + return joint_angles + + +def link_position_in_world_frame( + robot: typing.Any, + link_id: int, +): + """Computes the link's position in the world frame. + + Args: + robot: A robot instance. + link_id: The link id to calculate its position. + + Returns: + The position of the link in the world frame. + """ + return np.array( + robot.pybullet_client.getLinkState(robot.quadruped, link_id)[0]) + + +def link_position_in_base_frame( + robot: typing.Any, + link_id: int, +): + """Computes the link's local position in the robot frame. + + Args: + robot: A robot instance. + link_id: The link to calculate its relative position. + + Returns: + The relative position of the link. + """ + base_position, base_orientation = robot.GetBasePosition( + ), robot.GetBaseOrientation() + inverse_translation, inverse_rotation = robot.pybullet_client.invertTransform( + base_position, base_orientation) + + link_state = robot.pybullet_client.getLinkState(robot.quadruped, link_id) + link_position = link_state[0] + link_local_position, _ = robot.pybullet_client.multiplyTransforms( + inverse_translation, inverse_rotation, link_position, (0, 0, 0, 1)) + + return np.array(link_local_position) + + +def compute_jacobian( + robot: typing.Any, + link_id: int, +): + """Computes the Jacobian matrix for the given link. + + Args: + robot: A robot instance. + link_id: The link id as returned from loadURDF. + + Returns: + The 3 x N transposed Jacobian matrix. where N is the total DoFs of the + robot. For a quadruped, the first 6 columns of the matrix corresponds to + the CoM translation and rotation. The columns corresponds to a leg can be + extracted with indices [6 + leg_id * 3: 6 + leg_id * 3 + 3]. + """ + + all_joint_angles = [state[0] for state in robot.joint_states] + zero_vec = [0] * len(all_joint_angles) + jv, _ = robot.pybullet_client.calculateJacobian(robot.quadruped, link_id, + (0, 0, 0), all_joint_angles, + zero_vec, zero_vec) + jacobian = np.array(jv) + assert jacobian.shape[0] == 3 + return jacobian diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics_utils.py new file mode 100644 index 000000000..088f2bcb9 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/kinematics_utils.py @@ -0,0 +1,208 @@ +# Lint as: python3 +"""The inverse kinematic utilities.""" +from typing import Optional, Sequence + +import numpy as np + +from pybullet_utils import bullet_client + +_IDENTITY_ROTATION_QUAT = (0, 0, 0, 1) +_IK_SOLVER_TYPE = 0 +_LINK_POS_INDEX = 0 + + +def joint_angles_from_link_positions( + pybullet_client: bullet_client.BulletClient, + urdf_id: int, + link_ids: Sequence[int], + link_positions: Sequence[Sequence[float]], + positions_are_in_world_frame: bool = False, + joint_dof_ids: Optional[Sequence[int]] = None, +) -> np.ndarray: + """Uses Inverse Kinematics to calculate joint angles. + + Args: + pybullet_client: The bullet client. + urdf_id: The unique id returned after loading URDF. + link_ids: The link ids to compute the IK. + link_positions: The (x, y, z) of the links in the body or the world frame, + depending on whether the argument link_position_in_world_frame is true. + positions_are_in_world_frame: Whether the input link positions are specified + in the world frame or the robot's base frame. + joint_dof_ids: The degrees of freedom index of the joints we want to extract + the angles. This can be different from the joint unique ids. For example, + a fixed joint will increase the joint unique id but will not increase the + number of degree of freedoms. The joint dof id can be extracted in + PyBullet by getJointInfo, which corresponds to the "qIndex" in the + returned values. If not specified, will return all movable joint angles. + + Returns: + A list of joint angles. + """ + if positions_are_in_world_frame: + world_link_positions = link_positions + else: + # The PyBullet inverse Kinematics Calculation depends on the current URDF + # position/orientation, and we cannot pass them to the API. So we have to + # always query the current base position/orientation to compute world frame + # link positions. + urdf_base_position, urdf_base_orientation = ( + pybullet_client.getBasePositionAndOrientation(urdf_id)) + world_link_positions = [] + for link_position in link_positions: + world_link_position, _ = pybullet_client.multiplyTransforms( + urdf_base_position, urdf_base_orientation, link_position, + _IDENTITY_ROTATION_QUAT) + world_link_positions.append(world_link_position) + + # Notice that the API expects the link positions in the world frame. + all_joint_angles = pybullet_client.calculateInverseKinematics2( + urdf_id, link_ids, world_link_positions, solver=_IK_SOLVER_TYPE) + + # Extract the relevant joint angles. + if joint_dof_ids is None: + return np.array(all_joint_angles) + + return np.array([all_joint_angles[i] for i in joint_dof_ids]) + + +def link_position_in_world_frame( + pybullet_client: bullet_client.BulletClient, + urdf_id: int, + link_id: int, +): + """Computes the link's position in the world frame. + + Args: + pybullet_client: The bullet client. + urdf_id: The unique id returned after loading URDF. + link_id: The link id to calculate its position. + + Returns: + The position of the link in the world frame. + """ + return np.array(pybullet_client.getLinkState(urdf_id, link_id)[0]) + + +def link_position_in_base_frame( + pybullet_client: bullet_client.BulletClient, + urdf_id: int, + link_id: int, + base_link_id: Optional[int] = None, +): + """Computes the link's local position in the robot frame. + + Args: + pybullet_client: The bullet client. + urdf_id: The unique id returned after loading URDF. + link_id: The link to calculate its relative position. + base_link_id: The link id of the base. For the kinematics robot, such as + wheeled_robot_base, three additional joints are added to connect the world + and the base. The base_link_id is no longer -1, and need to be passed in. + + + Returns: + The relative position of the link. + """ + if base_link_id is None: + base_position, base_orientation = ( + pybullet_client.getBasePositionAndOrientation(urdf_id)) + else: + base_link_state = pybullet_client.getLinkState(urdf_id, base_link_id) + base_position, base_orientation = base_link_state[0], base_link_state[1] + + inverse_translation, inverse_rotation = pybullet_client.invertTransform( + base_position, base_orientation) + + link_state = pybullet_client.getLinkState(urdf_id, link_id) + link_position = link_state[0] + link_local_position, _ = pybullet_client.multiplyTransforms( + inverse_translation, inverse_rotation, link_position, (0, 0, 0, 1)) + + return np.array(link_local_position) + + +def compute_jacobian( + pybullet_client: bullet_client.BulletClient, + urdf_id: int, + link_id: int, + all_joint_positions: Sequence[float], + additional_translation: Optional[Sequence[float]] = (0, 0, 0), +) -> np.ndarray: + """Computes the Jacobian matrix for the given point on a link. + + CAVEAT: If during URDF loading process additional rotations are provided, the + computed Jacobian are also transformed. + + Args: + pybullet_client: The bullet client. + urdf_id: The unique id returned after loading URDF. + link_id: The link id as returned from loadURDF. + all_joint_positions: all the joint positions of the robot. This should + include the dummy/kinematic drive joints for the wheeled robot. + additional_translation: The additional translation of the point in the link + CoM frame. + + Returns: + The 3 x N transposed Jacobian matrix. where N is the total DoFs of the + robot. For a quadruped, the first 6 columns of the matrix corresponds to + the CoM translation and rotation. The columns corresponds to a leg can be + extracted with indices [6 + leg_id * 3: 6 + leg_id * 3 + 3]. + """ + + zero_vec = [0] * len(all_joint_positions) + jv, _ = pybullet_client.calculateJacobian( + urdf_id, + link_id, + additional_translation, + all_joint_positions, + objVelocities=zero_vec, + objAccelerations=zero_vec) + jacobian = np.array(jv) + assert jacobian.shape[0] == 3 + return jacobian + + +def rotate_to_base_frame( + pybullet_client: bullet_client.BulletClient, + urdf_id: int, + vector: Sequence[float], + init_orientation_inv_quat: Optional[Sequence[float]] = (0, 0, 0, 1) +) -> np.ndarray: + """Rotates the input vector to the base coordinate systems. + + Note: This is different from world frame to base frame transformation, as we + do not apply any translation here. + + Args: + pybullet_client: The bullet client. + urdf_id: The unique id returned after loading URDF. + vector: Input vector in the world frame. + init_orientation_inv_quat: + + Returns: + A rotated vector in the base frame. + """ + _, base_orientation_quat = ( + pybullet_client.getBasePositionAndOrientation(urdf_id)) + _, base_orientation_quat_from_init = pybullet_client.multiplyTransforms( + positionA=(0, 0, 0), + orientationA=init_orientation_inv_quat, + positionB=(0, 0, 0), + orientationB=base_orientation_quat) + _, inverse_base_orientation = pybullet_client.invertTransform( + [0, 0, 0], base_orientation_quat_from_init) + + # PyBullet transforms requires simple list/tuple or it may crash. + if isinstance(vector, np.ndarray): + vector_list = vector.tolist() + else: + vector_list = vector + + local_vector, _ = pybullet_client.multiplyTransforms( + positionA=(0, 0, 0), + orientationA=inverse_base_orientation, + positionB=vector_list, + orientationB=(0, 0, 0, 1), + ) + return np.array(local_vector) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/urdf_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/urdf_utils.py new file mode 100644 index 000000000..f45d4ff86 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/utilities/urdf_utils.py @@ -0,0 +1,59 @@ +# Lint as: python3 +"""Utilities for robot URDF models.""" + +from typing import Text + +# In the return from pybullet.getJointInfo, the name of the link whose parent is +# that joint. +LINK_NAME_INDEX = 12 +# Indication that link_name_to_id should return -1. This is a constant because +# different URDFs use different names for their base links. +BASE_LINK = "" + + +def link_name_to_id(link_name: Text, robot_id: int, pybullet_client) -> int: + """Returns the pybullet integer link id corresponding to link_name. + + Args: + link_name: The name of the link from the URDF. If this is BASE_LINK, returns + -1, the link id of the base according to pybullet convention. + robot_id: Integer id of the robot to which the link belongs, as returned by + pybullet.loadURDF(). + pybullet_client: Client in which the robot is loaded. + + Returns: + Integer id of the link. + + Raises: + ValueError if the link_name is not found in the robot. + """ + if link_name == BASE_LINK: + return -1 + link_name_list = [] + for i in range(pybullet_client.getNumJoints(robot_id)): + joint_info = pybullet_client.getJointInfo(robot_id, i) + link_name_i = joint_info[LINK_NAME_INDEX].decode("UTF-8") + if link_name_i == link_name: + return i + link_name_list.append(link_name_i) + raise ValueError("Link name '{}' not found in URDF. Options are: {}".format( + link_name, link_name_list)) + + +def set_collision_filter_group_mask(urdf_id: int, group: int, mask: int, + pybullet_client): + """Sets the collision filter group and mask to the robot. + + TODO(tingnan): Check if this has side effects with self collision flags + when loading URDF. + + Args: + urdf_id: The URDF id as returned by the loadURDF. + group: The collision group the robot is in. By default, all dynamics objects + in PyBullet use collision group 1. + mask: The collision bit mask to use. See go/pybullet for details. + pybullet_client: The bullet client to use. + """ + # We includes "-1" for the base link of the URDF. + for link_id in range(-1, pybullet_client.getNumJoints(urdf_id)): + pybullet_client.setCollisionFilterGroupMask(urdf_id, link_id, group, mask) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector.proto b/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector.proto new file mode 100644 index 000000000..92c48f859 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector.proto @@ -0,0 +1,84 @@ +syntax = "proto3"; + +package robotics.messages; + +option cc_enable_arenas = true; + +// A four-dimensional double precision vector. +message Vector4d { + double x = 1; + double y = 2; + double z = 3; + double w = 4; +} + +// A four-dimensional single precision vector. +message Vector4f { + float x = 1; + float y = 2; + float z = 3; + float w = 4; +} + +// A four-dimensional integer vector. +message Vector4i { + int64 x = 1; + int64 y = 2; + int64 z = 3; + int64 w = 4; +} + +// A three-dimensional double precision vector. +message Vector3d { + double x = 1; + double y = 2; + double z = 3; +} + +// A three-dimensional single precision vector. +message Vector3f { + float x = 1; + float y = 2; + float z = 3; +} + +// A three-dimensional integer vector. +message Vector3i { + int64 x = 1; + int64 y = 2; + int64 z = 3; +} + +// A two-dimensional double precision vector. +message Vector2d { + double x = 1; + double y = 2; +} + +// A two-dimensional single precision vector. +message Vector2f { + float x = 1; + float y = 2; +} + +// A two-dimensional integer vector. +message Vector2i { + int64 x = 1; + int64 y = 2; +} + +// Double precision vector of arbitrary size. +message Vectord { + repeated double data = 1 [packed = true]; +} + +// Single precision vector of arbitrary size. +message Vectorf { + repeated float data = 1 [packed = true]; +} + +// Integer vector of arbitrary size. +message Vectori { + repeated int64 data = 1 [packed = true]; +} + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector_pb2.py new file mode 100644 index 000000000..ccaad55b4 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/robots/vector_pb2.py @@ -0,0 +1,640 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: vector.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor.FileDescriptor( + name='vector.proto', + package='robotics.messages', + syntax='proto3', + serialized_options=b'\370\001\001', + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\x0cvector.proto\x12\x11robotics.messages\"6\n\x08Vector4d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\x12\t\n\x01w\x18\x04 \x01(\x01\"6\n\x08Vector4f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\"6\n\x08Vector4i\x12\t\n\x01x\x18\x01 \x01(\x03\x12\t\n\x01y\x18\x02 \x01(\x03\x12\t\n\x01z\x18\x03 \x01(\x03\x12\t\n\x01w\x18\x04 \x01(\x03\"+\n\x08Vector3d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\"+\n\x08Vector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\"+\n\x08Vector3i\x12\t\n\x01x\x18\x01 \x01(\x03\x12\t\n\x01y\x18\x02 \x01(\x03\x12\t\n\x01z\x18\x03 \x01(\x03\" \n\x08Vector2d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\" \n\x08Vector2f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\" \n\x08Vector2i\x12\t\n\x01x\x18\x01 \x01(\x03\x12\t\n\x01y\x18\x02 \x01(\x03\"\x1b\n\x07Vectord\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x01\x42\x02\x10\x01\"\x1b\n\x07Vectorf\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x02\x10\x01\"\x1b\n\x07Vectori\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x03\x42\x02\x10\x01\x42\x03\xf8\x01\x01\x62\x06proto3' +) + + + + +_VECTOR4D = _descriptor.Descriptor( + name='Vector4d', + full_name='robotics.messages.Vector4d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector4d.x', index=0, + number=1, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector4d.y', index=1, + number=2, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector4d.z', index=2, + number=3, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='w', full_name='robotics.messages.Vector4d.w', index=3, + number=4, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=35, + serialized_end=89, +) + + +_VECTOR4F = _descriptor.Descriptor( + name='Vector4f', + full_name='robotics.messages.Vector4f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector4f.x', index=0, + number=1, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector4f.y', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector4f.z', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='w', full_name='robotics.messages.Vector4f.w', index=3, + number=4, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=91, + serialized_end=145, +) + + +_VECTOR4I = _descriptor.Descriptor( + name='Vector4i', + full_name='robotics.messages.Vector4i', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector4i.x', index=0, + number=1, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector4i.y', index=1, + number=2, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector4i.z', index=2, + number=3, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='w', full_name='robotics.messages.Vector4i.w', index=3, + number=4, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=147, + serialized_end=201, +) + + +_VECTOR3D = _descriptor.Descriptor( + name='Vector3d', + full_name='robotics.messages.Vector3d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector3d.x', index=0, + number=1, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector3d.y', index=1, + number=2, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector3d.z', index=2, + number=3, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=203, + serialized_end=246, +) + + +_VECTOR3F = _descriptor.Descriptor( + name='Vector3f', + full_name='robotics.messages.Vector3f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector3f.x', index=0, + number=1, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector3f.y', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector3f.z', index=2, + number=3, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=248, + serialized_end=291, +) + + +_VECTOR3I = _descriptor.Descriptor( + name='Vector3i', + full_name='robotics.messages.Vector3i', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector3i.x', index=0, + number=1, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector3i.y', index=1, + number=2, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', full_name='robotics.messages.Vector3i.z', index=2, + number=3, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=293, + serialized_end=336, +) + + +_VECTOR2D = _descriptor.Descriptor( + name='Vector2d', + full_name='robotics.messages.Vector2d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector2d.x', index=0, + number=1, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector2d.y', index=1, + number=2, type=1, cpp_type=5, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=338, + serialized_end=370, +) + + +_VECTOR2F = _descriptor.Descriptor( + name='Vector2f', + full_name='robotics.messages.Vector2f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector2f.x', index=0, + number=1, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector2f.y', index=1, + number=2, type=2, cpp_type=6, label=1, + has_default_value=False, default_value=float(0), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=372, + serialized_end=404, +) + + +_VECTOR2I = _descriptor.Descriptor( + name='Vector2i', + full_name='robotics.messages.Vector2i', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='x', full_name='robotics.messages.Vector2i.x', index=0, + number=1, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', full_name='robotics.messages.Vector2i.y', index=1, + number=2, type=3, cpp_type=2, label=1, + has_default_value=False, default_value=0, + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=406, + serialized_end=438, +) + + +_VECTORD = _descriptor.Descriptor( + name='Vectord', + full_name='robotics.messages.Vectord', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='data', full_name='robotics.messages.Vectord.data', index=0, + number=1, type=1, cpp_type=5, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=b'\020\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=440, + serialized_end=467, +) + + +_VECTORF = _descriptor.Descriptor( + name='Vectorf', + full_name='robotics.messages.Vectorf', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='data', full_name='robotics.messages.Vectorf.data', index=0, + number=1, type=2, cpp_type=6, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=b'\020\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=469, + serialized_end=496, +) + + +_VECTORI = _descriptor.Descriptor( + name='Vectori', + full_name='robotics.messages.Vectori', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='data', full_name='robotics.messages.Vectori.data', index=0, + number=1, type=3, cpp_type=2, label=3, + has_default_value=False, default_value=[], + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + serialized_options=b'\020\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + serialized_options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[ + ], + serialized_start=498, + serialized_end=525, +) + +DESCRIPTOR.message_types_by_name['Vector4d'] = _VECTOR4D +DESCRIPTOR.message_types_by_name['Vector4f'] = _VECTOR4F +DESCRIPTOR.message_types_by_name['Vector4i'] = _VECTOR4I +DESCRIPTOR.message_types_by_name['Vector3d'] = _VECTOR3D +DESCRIPTOR.message_types_by_name['Vector3f'] = _VECTOR3F +DESCRIPTOR.message_types_by_name['Vector3i'] = _VECTOR3I +DESCRIPTOR.message_types_by_name['Vector2d'] = _VECTOR2D +DESCRIPTOR.message_types_by_name['Vector2f'] = _VECTOR2F +DESCRIPTOR.message_types_by_name['Vector2i'] = _VECTOR2I +DESCRIPTOR.message_types_by_name['Vectord'] = _VECTORD +DESCRIPTOR.message_types_by_name['Vectorf'] = _VECTORF +DESCRIPTOR.message_types_by_name['Vectori'] = _VECTORI +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +Vector4d = _reflection.GeneratedProtocolMessageType('Vector4d', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR4D, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector4d) + }) +_sym_db.RegisterMessage(Vector4d) + +Vector4f = _reflection.GeneratedProtocolMessageType('Vector4f', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR4F, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector4f) + }) +_sym_db.RegisterMessage(Vector4f) + +Vector4i = _reflection.GeneratedProtocolMessageType('Vector4i', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR4I, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector4i) + }) +_sym_db.RegisterMessage(Vector4i) + +Vector3d = _reflection.GeneratedProtocolMessageType('Vector3d', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR3D, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector3d) + }) +_sym_db.RegisterMessage(Vector3d) + +Vector3f = _reflection.GeneratedProtocolMessageType('Vector3f', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR3F, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector3f) + }) +_sym_db.RegisterMessage(Vector3f) + +Vector3i = _reflection.GeneratedProtocolMessageType('Vector3i', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR3I, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector3i) + }) +_sym_db.RegisterMessage(Vector3i) + +Vector2d = _reflection.GeneratedProtocolMessageType('Vector2d', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR2D, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector2d) + }) +_sym_db.RegisterMessage(Vector2d) + +Vector2f = _reflection.GeneratedProtocolMessageType('Vector2f', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR2F, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector2f) + }) +_sym_db.RegisterMessage(Vector2f) + +Vector2i = _reflection.GeneratedProtocolMessageType('Vector2i', (_message.Message,), { + 'DESCRIPTOR' : _VECTOR2I, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector2i) + }) +_sym_db.RegisterMessage(Vector2i) + +Vectord = _reflection.GeneratedProtocolMessageType('Vectord', (_message.Message,), { + 'DESCRIPTOR' : _VECTORD, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vectord) + }) +_sym_db.RegisterMessage(Vectord) + +Vectorf = _reflection.GeneratedProtocolMessageType('Vectorf', (_message.Message,), { + 'DESCRIPTOR' : _VECTORF, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vectorf) + }) +_sym_db.RegisterMessage(Vectorf) + +Vectori = _reflection.GeneratedProtocolMessageType('Vectori', (_message.Message,), { + 'DESCRIPTOR' : _VECTORI, + '__module__' : 'vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vectori) + }) +_sym_db.RegisterMessage(Vectori) + + +DESCRIPTOR._options = None +_VECTORD.fields_by_name['data']._options = None +_VECTORF.fields_by_name['data']._options = None +_VECTORI.fields_by_name['data']._options = None +# @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery.proto b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery.proto new file mode 100644 index 000000000..ef3a639d7 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery.proto @@ -0,0 +1,72 @@ +syntax = "proto3"; + +package robotics.reinforcement_learning.minitaur.vision; + +import "google/protobuf/timestamp.proto"; + +// The basic image protobuf. Used for RPC/IPC transmission. +message Image { + // The buffer that contains the actual image data. + bytes content = 1; + + // The image storage format. Can be raw or compressed types. + enum ImageFormat { + IMAGE_FORMAT_UNSPECIFIED = 0; + + // An 32-bit gray-scale image/matrix with row-major (height, width) memroy + // layout. Each pixel is a 32-bit floating-point number. + IMAGE_FORMAT_GRAY_HW_32F = 1; + + // An 8-bit BGRA raw image format, with HWC memory layout (e.g. the image is + // stored as a row-major matrix (height, width) of pixels, with each pixel + // an uint8[num_color_channels] packed array. This is the same format as + // CV_8UC4. + IMAGE_FORMAT_BGRA_HWC_8U = 2; + + // The 16-bit grayscale images with row-major memory layout. This is the + // default depth format for intel RealSense cameras. Each pixel is a 16 bit + // unsigned integer. + IMAGE_FORMAT_GRAY_HW_16U = 3; + + // An 8-bit RGB raw image format, with HWC memory layout. This is the + // default color format for intel RealSense cameras. + IMAGE_FORMAT_RGB_HWC_8U = 4; + + // TODO(tingnan): Add supports for different image formats like I420 or + // JPEG. + } + + ImageFormat image_format = 2; + + // The UTC time at which the image is taken. + google.protobuf.Timestamp timestamp = 3; + + // Image width and height in pixels. Critical for decoding raw images and + // optional for compressed JPEG/PNGs which already embed these information. + int32 width_px = 4; + int32 height_px = 5; +} + +// A captured frame from camera can combine multiple images from different +// streams, IR, depth, VGA. +message CameraFrame { + map images = 1; + string camera_id = 2; +} + +// Get the latest raw image from camera. +message GetFrameRequest { + // TODO: Also enable camera id in this proto. +} + +// Stacked frames. The imagery service can decide how many frames to transmit +// for each GetFrameRequest. +message CameraFrameCollection { + repeated CameraFrame frames = 1; +} + +// The capture session start/stop request. +message CaptureRequest { + string run_id = 1; + string logging_path = 2; +} diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_client.py b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_client.py new file mode 100644 index 000000000..ca2a2e689 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_client.py @@ -0,0 +1,137 @@ +"""The imagery client to connect to the camera job.""" + +from typing import Any, Dict, Sequence, Text +import gin + +from pybullet_envs.minitaur.fw_bridge import worker_builder +from pybullet_envs.minitaur.vision import imagery_pb2 +from pybullet_envs.minitaur.vision import imagery_utils +from google3.third_party.fluxworks.core.fluxworks.python.genericutil_py import fwassert +from google3.third_party.fluxworks.core.fluxworks.python.genericutil_py import timeutil + +_RPC_TIMEOUT = 1 * timeutil.TimeUtil.SEC + +_URI_START_CAPTURE = "fwuri://VisionJob/StartCapture" +_URI_STOP_CAPTURE = "fwuri://VisionJob/StopCapture" +_URI_GET_FRAME = "fwuri://VisionJob/GetFrame" + + +@gin.configurable +class ImageryClient(object): + """Sends commands and receives states from cameras.""" + + def __init__( + self, + fw_worker=None, + rpc_timeout_sec=_RPC_TIMEOUT, + ip_address=None, + port=None, + async_mode=False, + start_capture_uri: Text = _URI_START_CAPTURE, + stop_capture_uri: Text = _URI_STOP_CAPTURE, + get_frame_uri: Text = _URI_GET_FRAME, + ): + """Initializes the client. + + Args: + fw_worker: A FluxWorks worker instance. + rpc_timeout_sec: The timeout for any RPC calls from this client. + ip_address: The ip address of the camera/vision process. If vision job is + also instantiated in the same FluxWorks worker, both ip address and port + are not needed. + port: The port of the camera/vision process. + async_mode: Whether the RPC calls in this client are async or synchronous. + Aync mode is only required when you have multiple workers communicating + with each other in the same Python process. If worker A is calling + worker B's RPC, worker B's RPC is trying to get GIL from it's thread but + caller (worker A) already holds the GIL, and this will cause a dead lock + if worker A's calls are synchronous. If worker A is calling its own RPC, + the same GIL can be used so there is no dead lock, and there is no need + for async mode. Async mode will require context switching and thus is a + bit slower. + start_capture_uri: The FluxWorks URI to start camera capture. + stop_capture_uri: The FluxWorks URI to stop camera capture. + get_frame_uri: The FluxWorks URI to get camera frames. + """ + self._rpc_timeout_sec = rpc_timeout_sec + if fw_worker is None: + fw_worker = worker_builder.GetDefaultWorker() + self._worker = fw_worker + + # TODO(tingnan): Use a single address and split the string for FW. + if ip_address is not None: + self._worker.ConnectToWorker(ip_address, port) + + self._async_mode = async_mode + self._start_capture_uri = start_capture_uri + self._stop_capture_uri = stop_capture_uri + self._get_frame_uri = get_frame_uri + + def _convert_camera_frame_to_image_dict( + self, camera_frame: imagery_pb2.CameraFrame): + """Converts the camera frame to an image dictionary.""" + # Each camera frame might contain multiple image channels, such as rgb and + # depth. + images = {} + for image_name, image_proto in camera_frame.images.items(): + image_array = imagery_utils.convert_image_to_array(image_proto) + images[image_name] = image_array + return images + + def start_capture(self, run_id: Text = "vision"): + """Starts the camera capture session. + + Args: + run_id: The capture session id. This id will determine the name of the + image logs' sub-direcotry. + """ + capture_request = imagery_pb2.CaptureRequest() + capture_request.run_id = run_id + fwassert.FwAssert.CheckErrorMessage( + self._worker.CallOnewayProtoRpc( + self._start_capture_uri, + capture_request, + async_mode=self._async_mode)) + + def stop_capture(self): + """Concludes the current capture session.""" + capture_request = imagery_pb2.CaptureRequest() + fwassert.FwAssert.CheckErrorMessage( + self._worker.CallOnewayProtoRpc( + self._stop_capture_uri, + capture_request, + async_mode=self._async_mode)) + + def get_camera_images(self) -> Dict[Text, Sequence[Any]]: + """Gets the latest camera images. + + Camera images can only be obtained after self.start_capture() is called. + + Returns: + A dictionary of camera frames, with the camera id as the key. Each camera + frame may contain multiple streams. For example, on a realsense camera we + may have "rgb" and "depth" streams, depending on the configuration. + """ + get_frame_request = imagery_pb2.GetFrameRequest() + frame_collection = imagery_pb2.CameraFrameCollection() + fwassert.FwAssert.CheckErrorMessage( + self._worker.CallRoundtripProtoRpc( + self._get_frame_uri, + get_frame_request, + frame_collection, + self._rpc_timeout_sec, + async_mode=self._async_mode)) + + images_by_camera = {} + for camera_frame in frame_collection.frames: + camera_id = camera_frame.camera_id + # In case we received multiple frames, we apppend them in the order + # received. + if camera_id in images_by_camera: + images_by_camera[camera_id].append( + self._convert_camera_frame_to_image_dict(camera_frame)) + else: + images_by_camera[camera_id] = [ + self._convert_camera_frame_to_image_dict(camera_frame) + ] + return images_by_camera diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils.py b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils.py new file mode 100644 index 000000000..ccf8da044 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils.py @@ -0,0 +1,43 @@ +"""Utilities to convert imagery protobufs to other formats.""" + +import numpy as np + +from pybullet_envs.minitaur.vision import imagery_pb2 + + +# TODO(b/123306148): Support the conversion from image array to the proto. +def convert_image_to_array(image): + """Converts an Image proto into a numpy array. + + Args: + image: An instance of the imagery_pb2.Image proto. + + Returns: + A numpy array. For color images (e.g. BGRA), the converted ND array + has the format [Height, Width, Channel]. For gray images (e.g. depth), the + converted ND array has the format [Height, Width]. + """ + + if image.image_format == imagery_pb2.Image.IMAGE_FORMAT_BGRA_HWC_8U: + img_buffer = np.fromstring(image.content, dtype=np.uint8) + img = np.reshape( + img_buffer, [image.height_px, image.width_px, 4], order="C") + return img + + if image.image_format == imagery_pb2.Image.IMAGE_FORMAT_RGB_HWC_8U: + img_buffer = np.fromstring(image.content, dtype=np.uint8) + img = np.reshape( + img_buffer, [image.height_px, image.width_px, 3], order="C") + return img + + if image.image_format == imagery_pb2.Image.IMAGE_FORMAT_GRAY_HW_32F: + img_buffer = np.fromstring(image.content, dtype=np.float32) + img = np.reshape(img_buffer, [image.height_px, image.width_px], order="C") + return img + + if image.image_format == imagery_pb2.Image.IMAGE_FORMAT_GRAY_HW_16U: + img_buffer = np.fromstring(image.content, dtype=np.uint16) + img = np.reshape(img_buffer, [image.height_px, image.width_px], order="C") + return img + + raise ValueError("Unsupported image format {}".format(image.image_format)) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils_test.py new file mode 100644 index 000000000..5b5493f64 --- /dev/null +++ b/examples/pybullet/gym/pybullet_envs/minitaur/vision/imagery_utils_test.py @@ -0,0 +1,84 @@ +"""Tests for imagery_utils.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import struct +import numpy as np + +from pybullet_envs.minitaur.vision import imagery_pb2 +from pybullet_envs.minitaur.vision import imagery_utils +from google3.testing.pybase import googletest + + +class ImageryUtilsTest(googletest.TestCase): + + def test_convert_bgra_images(self): + image = imagery_pb2.Image( + height_px=2, + width_px=2, + image_format=imagery_pb2.Image.IMAGE_FORMAT_BGRA_HWC_8U, + content=b'ABCDABCDABCDABCD', + ) + + image_array = imagery_utils.convert_image_to_array(image) + + self.assertEqual(image_array.dtype, np.uint8) + self.assertEqual(image_array.shape, (image.height_px, image.width_px, 4)) + self.assertEqual(image_array[0, 0, 0], ord('A')) + self.assertEqual(image_array[1, 0, 3], ord('D')) + + def test_convert_rgb_images(self): + image = imagery_pb2.Image( + height_px=2, + width_px=2, + image_format=imagery_pb2.Image.IMAGE_FORMAT_RGB_HWC_8U, + content=b'ABCABCABCABC', + ) + + image_array = imagery_utils.convert_image_to_array(image) + + self.assertEqual(image_array.dtype, np.uint8) + self.assertEqual(image_array.shape, (image.height_px, image.width_px, 3)) + self.assertEqual(image_array[0, 0, 0], ord('A')) + self.assertEqual(image_array[1, 1, 2], ord('C')) + + def test_convert_gray_32bit_images(self): + image = imagery_pb2.Image( + height_px=2, + width_px=3, + image_format=imagery_pb2.Image.IMAGE_FORMAT_GRAY_HW_32F, + content=b'AAAABBBBCCCCAAAABBBBCCCC', + ) + + image_array = imagery_utils.convert_image_to_array(image) + + self.assertEqual(image_array.dtype, np.float32) + self.assertEqual(image_array.shape, (image.height_px, image.width_px)) + self.assertEqual(image_array[0, 2], struct.unpack(b' Date: Mon, 5 Apr 2021 11:40:45 -0700 Subject: [PATCH 30/36] Cleaned-up/fixed version of this Pull Request #3239, thanks to Wenlong Lu --- examples/SharedMemory/PhysicsClientC_API.cpp | 62 ++++++++ examples/SharedMemory/PhysicsClientC_API.h | 4 + .../PhysicsServerCommandProcessor.cpp | 122 +++++++++++++--- examples/SharedMemory/SharedMemoryCommands.h | 3 + examples/pybullet/pybullet.c | 132 ++++++++++++++++-- .../Featherstone/btMultiBodyConstraint.cpp | 5 +- .../Featherstone/btMultiBodyConstraint.h | 2 +- .../btMultiBodySphericalJointMotor.cpp | 20 ++- .../btMultiBodySphericalJointMotor.h | 49 ++++++- 9 files changed, 358 insertions(+), 41 deletions(-) diff --git a/examples/SharedMemory/PhysicsClientC_API.cpp b/examples/SharedMemory/PhysicsClientC_API.cpp index 99b907a70..c70889872 100644 --- a/examples/SharedMemory/PhysicsClientC_API.cpp +++ b/examples/SharedMemory/PhysicsClientC_API.cpp @@ -1056,6 +1056,7 @@ B3_SHARED_API b3SharedMemoryCommandHandle b3JointControlCommandInit2Internal(b3S command->m_sendDesiredStateCommandArgument.m_Kp[i] = 0; command->m_sendDesiredStateCommandArgument.m_Kd[i] = 0; command->m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[i] = 0; + command->m_sendDesiredStateCommandArgument.m_damping[i] = 0; } command->m_sendDesiredStateCommandArgument.m_desiredStateQ[3] = 1; return (b3SharedMemoryCommandHandle)command; @@ -1103,6 +1104,22 @@ B3_SHARED_API int b3JointControlSetKp(b3SharedMemoryCommandHandle commandHandle, return 0; } +B3_SHARED_API int b3JointControlSetKpMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* kps, int dofCount) +{ + struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; + b3Assert(command); + if ((dofIndex >= 0) && (dofIndex < MAX_DEGREE_OF_FREEDOM ) && dofCount >= 0 && dofCount <= 4) + { + for (int dof = 0; dof < dofCount; dof++) + { + command->m_sendDesiredStateCommandArgument.m_Kp[dofIndex + dof] = kps[dof]; + command->m_updateFlags |= SIM_DESIRED_STATE_HAS_KP; + command->m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[dofIndex + dof] |= SIM_DESIRED_STATE_HAS_KP; + } + } + return 0; +} + B3_SHARED_API int b3JointControlSetKd(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value) { struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; @@ -1116,6 +1133,22 @@ B3_SHARED_API int b3JointControlSetKd(b3SharedMemoryCommandHandle commandHandle, return 0; } +B3_SHARED_API int b3JointControlSetKdMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* kds, int dofCount) +{ + struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; + b3Assert(command); + if ((dofIndex >= 0) && (dofIndex < MAX_DEGREE_OF_FREEDOM ) && dofCount >= 0 && dofCount <= 4) + { + for (int dof = 0; dof < dofCount; dof++) + { + command->m_sendDesiredStateCommandArgument.m_Kd[dofIndex + dof] = kds[dof]; + command->m_updateFlags |= SIM_DESIRED_STATE_HAS_KD; + command->m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[dofIndex + dof] |= SIM_DESIRED_STATE_HAS_KD; + } + } + return 0; +} + B3_SHARED_API int b3JointControlSetMaximumVelocity(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double maximumVelocity) { struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; @@ -1174,6 +1207,35 @@ B3_SHARED_API int b3JointControlSetDesiredForceTorqueMultiDof(b3SharedMemoryComm return 0; } +B3_SHARED_API int b3JointControlSetDamping(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value) +{ + struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; + b3Assert(command); + if ((dofIndex >= 0) && (dofIndex < MAX_DEGREE_OF_FREEDOM)) + { + command->m_sendDesiredStateCommandArgument.m_damping[dofIndex] = value; + command->m_updateFlags |= SIM_DESIRED_STATE_HAS_DAMPING; + command->m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[dofIndex] |= SIM_DESIRED_STATE_HAS_DAMPING; + } + return 0; +} + +B3_SHARED_API int b3JointControlSetDampingMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* damping, int dofCount) +{ + struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; + b3Assert(command); + if ((dofIndex >= 0) && (dofIndex < MAX_DEGREE_OF_FREEDOM ) && dofCount >= 0 && dofCount <= 4) + { + for (int dof = 0; dof < dofCount; dof++) + { + command->m_sendDesiredStateCommandArgument.m_damping[dofIndex+dof] = damping[dof]; + command->m_updateFlags |= SIM_DESIRED_STATE_HAS_DAMPING; + command->m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[dofIndex + dof] |= SIM_DESIRED_STATE_HAS_DAMPING; + } + } + return 0; +} + B3_SHARED_API int b3JointControlSetMaximumForce(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value) { struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle; diff --git a/examples/SharedMemory/PhysicsClientC_API.h b/examples/SharedMemory/PhysicsClientC_API.h index 4727d6f86..101868028 100644 --- a/examples/SharedMemory/PhysicsClientC_API.h +++ b/examples/SharedMemory/PhysicsClientC_API.h @@ -484,7 +484,9 @@ extern "C" B3_SHARED_API int b3JointControlSetDesiredPositionMultiDof(b3SharedMemoryCommandHandle commandHandle, int qIndex, const double* position, int dofCount); B3_SHARED_API int b3JointControlSetKp(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value); + B3_SHARED_API int b3JointControlSetKpMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* kps, int dofCount); B3_SHARED_API int b3JointControlSetKd(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value); + B3_SHARED_API int b3JointControlSetKdMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* kds, int dofCount); B3_SHARED_API int b3JointControlSetMaximumVelocity(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double maximumVelocity); ///Only use when controlMode is CONTROL_MODE_VELOCITY @@ -494,6 +496,8 @@ extern "C" B3_SHARED_API int b3JointControlSetMaximumForce(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value); B3_SHARED_API int b3JointControlSetDesiredForceTorqueMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* forces, int dofCount); + B3_SHARED_API int b3JointControlSetDamping(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value); + B3_SHARED_API int b3JointControlSetDampingMultiDof(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double* damping, int dofCount); ///Only use if when controlMode is CONTROL_MODE_TORQUE, B3_SHARED_API int b3JointControlSetDesiredForceTorque(b3SharedMemoryCommandHandle commandHandle, int dofIndex, double value); diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp index 14f1e2b4b..88b50fcca 100644 --- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp +++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp @@ -7143,8 +7143,8 @@ bool PhysicsServerCommandProcessor::processSendDesiredStateCommand(const struct motor->setRhsClamp(clientCmd.m_sendDesiredStateCommandArgument.m_rhsClamp[velIndex]); } bool hasDesiredPosOrVel = false; - btScalar kp = 0.f; - btScalar kd = 0.f; + btVector3 kp(0, 0, 0); + btVector3 kd(0, 0, 0); btVector3 desiredVelocity(0, 0, 0); if ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex] & SIM_DESIRED_STATE_HAS_QDOT) != 0) { @@ -7153,7 +7153,7 @@ bool PhysicsServerCommandProcessor::processSendDesiredStateCommand(const struct clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQdot[velIndex + 0], clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQdot[velIndex + 1], clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQdot[velIndex + 2]); - kd = 0.1; + kd.setValue(0.1, 0.1, 0.1); } btQuaternion desiredPosition(0, 0, 0, 1); if ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[posIndex] & SIM_DESIRED_STATE_HAS_Q) != 0) @@ -7164,38 +7164,122 @@ bool PhysicsServerCommandProcessor::processSendDesiredStateCommand(const struct clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQ[posIndex + 1], clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQ[posIndex + 2], clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateQ[posIndex + 3]); - kp = 0.1; + kp.setValue(0.1, 0.1, 0.1); } if (hasDesiredPosOrVel) { + bool useMultiDof = true; + if ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex] & SIM_DESIRED_STATE_HAS_KP) != 0) { - kp = clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex]; + kp.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 0]); + } + if (((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+0] & SIM_DESIRED_STATE_HAS_KP) != 0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+1] & SIM_DESIRED_STATE_HAS_KP) != 0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+2] & SIM_DESIRED_STATE_HAS_KP) != 0) + ) + { + kp.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 1], + clientCmd.m_sendDesiredStateCommandArgument.m_Kp[velIndex + 2]); + } else + { + useMultiDof = false; } if ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex] & SIM_DESIRED_STATE_HAS_KD) != 0) { - kd = clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex]; + kd.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 0]); } - motor->setVelocityTarget(desiredVelocity, kd); - //todo: instead of clamping, combine the motor and limit - //and combine handling of limit force and motor force. + if (((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+0] & SIM_DESIRED_STATE_HAS_KD) != 0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+1] & SIM_DESIRED_STATE_HAS_KD) != 0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+2] & SIM_DESIRED_STATE_HAS_KD) != 0)) + { + kd.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 1], + clientCmd.m_sendDesiredStateCommandArgument.m_Kd[velIndex + 2]); + } else + { + useMultiDof = false; + } - //clamp position - //if (mb->getLink(link).m_jointLowerLimit <= mb->getLink(link).m_jointUpperLimit) - //{ - // btClamp(desiredPosition, mb->getLink(link).m_jointLowerLimit, mb->getLink(link).m_jointUpperLimit); - //} - motor->setPositionTarget(desiredPosition, kp); + btVector3 maxImp( + 1000000.f * m_data->m_physicsDeltaTime, + 1000000.f * m_data->m_physicsDeltaTime, + 1000000.f * m_data->m_physicsDeltaTime); - btScalar maxImp = 1000000.f * m_data->m_physicsDeltaTime; + if ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex] & SIM_DESIRED_STATE_HAS_MAX_FORCE)!=0) + { + maxImp.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 0] * m_data->m_physicsDeltaTime, + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 0] * m_data->m_physicsDeltaTime, + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 0] * m_data->m_physicsDeltaTime); + } - if ((clientCmd.m_updateFlags & SIM_DESIRED_STATE_HAS_MAX_FORCE) != 0) - maxImp = clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex] * m_data->m_physicsDeltaTime; + if (((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+0] & SIM_DESIRED_STATE_HAS_MAX_FORCE)!=0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+1] & SIM_DESIRED_STATE_HAS_MAX_FORCE)!=0) && + ((clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+2] & SIM_DESIRED_STATE_HAS_MAX_FORCE)!=0)) + { + maxImp.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 0] * m_data->m_physicsDeltaTime, + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 1] * m_data->m_physicsDeltaTime, + clientCmd.m_sendDesiredStateCommandArgument.m_desiredStateForceTorque[velIndex + 2] * m_data->m_physicsDeltaTime); + } else + { + useMultiDof = false; + } + + if (useMultiDof) + { + motor->setVelocityTargetMultiDof(desiredVelocity, kd); + motor->setPositionTargetMultiDof(desiredPosition, kp); + motor->setMaxAppliedImpulseMultiDof(maxImp); + } else + { + motor->setVelocityTarget(desiredVelocity, kd[0]); + //todo: instead of clamping, combine the motor and limit + //and combine handling of limit force and motor force. - motor->setMaxAppliedImpulse(maxImp); + //clamp position + //if (mb->getLink(link).m_jointLowerLimit <= mb->getLink(link).m_jointUpperLimit) + //{ + // btClamp(desiredPosition, mb->getLink(link).m_jointLowerLimit, mb->getLink(link).m_jointUpperLimit); + //} + motor->setPositionTarget(desiredPosition, kp[0]); + motor->setMaxAppliedImpulse(maxImp[0]); + } + + btVector3 damping(1.f, 1.f, 1.f); + if ((clientCmd.m_updateFlags & SIM_DESIRED_STATE_HAS_DAMPING) != 0) { + if ( + (clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+0] & SIM_DESIRED_STATE_HAS_DAMPING)&& + (clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+1] & SIM_DESIRED_STATE_HAS_DAMPING)&& + (clientCmd.m_sendDesiredStateCommandArgument.m_hasDesiredStateFlags[velIndex+2] & SIM_DESIRED_STATE_HAS_DAMPING) + ) + { + damping.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 1], + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 2]); + } else + { + damping.setValue( + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 0], + clientCmd.m_sendDesiredStateCommandArgument.m_damping[velIndex + 0]); + } + } + motor->setDamping(damping); } numMotors++; } diff --git a/examples/SharedMemory/SharedMemoryCommands.h b/examples/SharedMemory/SharedMemoryCommands.h index a019a58ca..e463b2dc5 100644 --- a/examples/SharedMemory/SharedMemoryCommands.h +++ b/examples/SharedMemory/SharedMemoryCommands.h @@ -465,6 +465,8 @@ struct SendDesiredStateArgs //or the maximum applied force/torque for the PD/motor/constraint to reach the desired velocity in CONTROL_MODE_VELOCITY and CONTROL_MODE_POSITION_VELOCITY_PD mode //indexed by degree of freedom, 6 dof base, and then dofs for each link double m_desiredStateForceTorque[MAX_DEGREE_OF_FREEDOM]; + + double m_damping[MAX_DEGREE_OF_FREEDOM]; }; enum EnumSimDesiredStateUpdateFlags @@ -475,6 +477,7 @@ enum EnumSimDesiredStateUpdateFlags SIM_DESIRED_STATE_HAS_KP = 8, SIM_DESIRED_STATE_HAS_MAX_FORCE = 16, SIM_DESIRED_STATE_HAS_RHS_CLAMP = 32, + SIM_DESIRED_STATE_HAS_DAMPING = 64, }; enum EnumSimParamUpdateFlags diff --git a/examples/pybullet/pybullet.c b/examples/pybullet/pybullet.c index 7359fdadc..d4d45db6e 100644 --- a/examples/pybullet/pybullet.c +++ b/examples/pybullet/pybullet.c @@ -51,9 +51,10 @@ #pragma message(B3_VAR_NAME_VALUE(PY_MAJOR_VERSION)) #pragma message(B3_VAR_NAME_VALUE(PY_MINOR_VERSION)) #endif - +//#define PYBULLET_USE_NUMPY #ifdef PYBULLET_USE_NUMPY #include +//#include "C:/Python37/Lib/site-packages/numpy/core/include/numpy/arrayobject.h" #endif #if PY_MAJOR_VERSION >= 3 @@ -3234,17 +3235,29 @@ static PyObject* pybullet_setJointMotorControlMultiDof(PyObject* self, PyObject* PyObject* targetVelocityObj = 0; PyObject* targetForceObj = 0; - double kp = 0.1; - double kd = 1.0; + double kpArray[3] = {0.1, 0.1, 0.1}; + int kpSize = 0; + PyObject* kpObj = 0; + double kdArray[3] = {1.0, 1.0, 1.0}; + int kdSize = 0; + PyObject* kdObj = 0; double maxVelocity = -1; + double dampingArray[3] = {1.0, 1.0, 1.0}; + int dampingSize = 0; + PyObject* dampingObj = 0; b3PhysicsClientHandle sm = 0; int physicsClientId = 0; - static char* kwlist[] = {"bodyUniqueId", "jointIndex", "controlMode", "targetPosition", "targetVelocity", "force", "positionGain", "velocityGain", "maxVelocity", "physicsClientId", NULL}; - if (!PyArg_ParseTupleAndKeywords(args, keywds, "iii|OOOdddi", kwlist, &bodyUniqueId, &jointIndex, &controlMode, - &targetPositionObj, &targetVelocityObj, &targetForceObj, &kp, &kd, &maxVelocity, &physicsClientId)) + static char* kwlist[] = {"bodyUniqueId", "jointIndex", "controlMode", "targetPosition", "targetVelocity", "force", "positionGain", "velocityGain", "maxVelocity", "damping", "physicsClientId", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, keywds, "iii|OOOddddi", kwlist, &bodyUniqueId, &jointIndex, &controlMode, + &targetPositionObj, &targetVelocityObj, &targetForceObj, &kpArray[0], &kdArray[0], &maxVelocity, &dampingArray[0], &physicsClientId)) { - return NULL; + PyErr_Clear(); + if (!PyArg_ParseTupleAndKeywords(args, keywds, "iii|OOOOOdOi", kwlist, &bodyUniqueId, &jointIndex, &controlMode, + &targetPositionObj, &targetVelocityObj, &targetForceObj, &kpObj, &kdObj, &maxVelocity, &dampingObj, &physicsClientId)) + { + return NULL; + } } sm = getPhysicsClient(physicsClientId); if (sm == 0) @@ -3328,6 +3341,81 @@ static PyObject* pybullet_setJointMotorControlMultiDof(PyObject* self, PyObject* } } + if (kpObj) + { + int i = 0; + PyObject* kpSeq = 0; + kpSeq = PySequence_Fast(kpObj, "expected a kp sequence"); + kpSize = PySequence_Size(kpObj); + + if (kpSize < 0) + { + kpSize = 0; + } + if (kpSize > 3) + { + kpSize = 3; + } + if (kpSeq) + { + for (i = 0; i < kpSize; i++) + { + kpArray[i] = pybullet_internalGetFloatFromSequence(kpSeq, i); + } + Py_DECREF(kpSeq); + } + } + + if (kdObj) + { + int i = 0; + PyObject* kdSeq = 0; + kdSeq = PySequence_Fast(kdObj, "expected a kd sequence"); + kdSize = PySequence_Size(kdObj); + + if (kdSize < 0) + { + kdSize = 0; + } + if (kdSize > 3) + { + kdSize = 3; + } + if (kdSeq) + { + for (i = 0; i < kdSize; i++) + { + kdArray[i] = pybullet_internalGetFloatFromSequence(kdSeq, i); + } + Py_DECREF(kdSeq); + } + } + + if (dampingObj) + { + int i = 0; + PyObject* dampingSeq = 0; + dampingSeq = PySequence_Fast(dampingObj, "expected a damping sequence"); + dampingSize = PySequence_Size(dampingObj); + + if (dampingSize < 0) + { + dampingSize = 0; + } + if (dampingSize > 3) + { + dampingSize = 3; + } + if (dampingSeq) + { + for (i = 0; i < dampingSize; i++) + { + dampingArray[i] = pybullet_internalGetFloatFromSequence(dampingSeq, i); + } + Py_DECREF(dampingSeq); + } + } + //if (targetPositionSize == 0 && targetVelocitySize == 0) //{ @@ -3400,7 +3488,15 @@ static PyObject* pybullet_setJointMotorControlMultiDof(PyObject* self, PyObject* //printf("Warning: targetPosition array size doesn't match joint position size (got %d, expected %d).",targetPositionSize, info.m_qSize); } - b3JointControlSetKp(commandHandle, info.m_uIndex, kp); + if (info.m_uSize == kpSize || kpSize == 1) + { + b3JointControlSetKpMultiDof(commandHandle, info.m_uIndex, + kpArray, kpSize); + } + else if (kpSize == 0) + { + b3JointControlSetKp(commandHandle, info.m_uIndex, kpArray[0]); + } if (info.m_uSize == targetVelocitySize) { b3JointControlSetDesiredVelocityMultiDof(commandHandle, info.m_uIndex, @@ -3410,12 +3506,30 @@ static PyObject* pybullet_setJointMotorControlMultiDof(PyObject* self, PyObject* { //printf("Warning: targetVelocity array size doesn't match joint dimentions (got %d, expected %d).", targetVelocitySize, info.m_uSize); } - b3JointControlSetKd(commandHandle, info.m_uIndex, kd); + if (info.m_uSize == kdSize || kdSize == 1) + { + b3JointControlSetKdMultiDof(commandHandle, info.m_uIndex, + kdArray, kdSize); + } + else if (kdSize == 0) + { + b3JointControlSetKd(commandHandle, info.m_uIndex, kdArray[0]); + } if (info.m_uSize == targetForceSize || targetForceSize == 1) { b3JointControlSetDesiredForceTorqueMultiDof(commandHandle, info.m_uIndex, targetForceArray, targetForceSize); } + if (info.m_uSize == dampingSize || dampingSize == 1) + { + b3JointControlSetDampingMultiDof(commandHandle, info.m_uIndex, + dampingArray, dampingSize); + } + else if (dampingSize == 0) + { + b3JointControlSetDamping(commandHandle, info.m_uIndex, + dampingArray[0]); + } break; } default: diff --git a/src/BulletDynamics/Featherstone/btMultiBodyConstraint.cpp b/src/BulletDynamics/Featherstone/btMultiBodyConstraint.cpp index 1ba586114..00d5fd560 100644 --- a/src/BulletDynamics/Featherstone/btMultiBodyConstraint.cpp +++ b/src/BulletDynamics/Featherstone/btMultiBodyConstraint.cpp @@ -61,7 +61,8 @@ btScalar btMultiBodyConstraint::fillMultiBodyConstraint(btMultiBodySolverConstra btScalar lowerLimit, btScalar upperLimit, bool angConstraint, btScalar relaxation, - bool isFriction, btScalar desiredVelocity, btScalar cfmSlip) + bool isFriction, btScalar desiredVelocity, btScalar cfmSlip, + btScalar damping) { solverConstraint.m_multiBodyA = m_bodyA; solverConstraint.m_multiBodyB = m_bodyB; @@ -348,7 +349,7 @@ btScalar btMultiBodyConstraint::fillMultiBodyConstraint(btMultiBodySolverConstra { btScalar positionalError = 0.f; - btScalar velocityError = desiredVelocity - rel_vel; // * damping; + btScalar velocityError = (desiredVelocity - rel_vel) * damping; btScalar erp = infoGlobal.m_erp2; diff --git a/src/BulletDynamics/Featherstone/btMultiBodyConstraint.h b/src/BulletDynamics/Featherstone/btMultiBodyConstraint.h index 4a6007ee3..1aaa07b69 100644 --- a/src/BulletDynamics/Featherstone/btMultiBodyConstraint.h +++ b/src/BulletDynamics/Featherstone/btMultiBodyConstraint.h @@ -94,7 +94,7 @@ protected: bool angConstraint = false, btScalar relaxation = 1.f, - bool isFriction = false, btScalar desiredVelocity = 0, btScalar cfmSlip = 0); + bool isFriction = false, btScalar desiredVelocity = 0, btScalar cfmSlip = 0, btScalar damping = 1.0); public: BT_DECLARE_ALIGNED_ALLOCATOR(); diff --git a/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.cpp b/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.cpp index 25ddd539b..00a7ef357 100644 --- a/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.cpp +++ b/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.cpp @@ -26,10 +26,13 @@ btMultiBodySphericalJointMotor::btMultiBodySphericalJointMotor(btMultiBody* body : btMultiBodyConstraint(body, body, link, body->getLink(link).m_parent, 3, true, MULTIBODY_CONSTRAINT_SPHERICAL_MOTOR), m_desiredVelocity(0, 0, 0), m_desiredPosition(0,0,0,1), - m_kd(1.), - m_kp(0.2), + m_use_multi_dof_params(false), + m_kd(1., 1., 1.), + m_kp(0.2, 0.2, 0.2), m_erp(1), - m_rhsClamp(SIMD_INFINITY) + m_rhsClamp(SIMD_INFINITY), + m_maxAppliedImpulseMultiDof(maxMotorImpulse, maxMotorImpulse, maxMotorImpulse), + m_damping(1.0, 1.0, 1.0) { m_maxAppliedImpulse = maxMotorImpulse; @@ -139,7 +142,8 @@ btQuaternion relRot = currentQuat.inverse() * desiredQuat; btScalar currentVelocity = m_bodyA->getJointVelMultiDof(m_linkA)[dof]; btScalar desiredVelocity = this->m_desiredVelocity[row]; - btScalar velocityError = desiredVelocity - currentVelocity; + double kd = m_use_multi_dof_params ? m_kd[row % 3] : m_kd[0]; + btScalar velocityError = (desiredVelocity - currentVelocity) * kd; btMatrix3x3 frameAworld; frameAworld.setIdentity(); @@ -152,12 +156,16 @@ btQuaternion relRot = currentQuat.inverse() * desiredQuat; case btMultibodyLink::eSpherical: { btVector3 constraintNormalAng = frameAworld.getColumn(row % 3); - posError = m_kp*angleDiff[row % 3]; + double kp = m_use_multi_dof_params ? m_kp[row % 3] : m_kp[0]; + posError = kp*angleDiff[row % 3]; + double max_applied_impulse = m_use_multi_dof_params ? m_maxAppliedImpulseMultiDof[row % 3] : m_maxAppliedImpulse; fillMultiBodyConstraint(constraintRow, data, 0, 0, constraintNormalAng, btVector3(0,0,0), dummy, dummy, posError, infoGlobal, - -m_maxAppliedImpulse, m_maxAppliedImpulse, true); + -max_applied_impulse, max_applied_impulse, true, + 1.0, false, 0, 0, + m_damping[row % 3]); constraintRow.m_orgConstraint = this; constraintRow.m_orgDofIndex = row; break; diff --git a/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.h b/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.h index 621beab5a..bdeccc2e2 100644 --- a/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.h +++ b/src/BulletDynamics/Featherstone/btMultiBodySphericalJointMotor.h @@ -26,10 +26,13 @@ class btMultiBodySphericalJointMotor : public btMultiBodyConstraint protected: btVector3 m_desiredVelocity; btQuaternion m_desiredPosition; - btScalar m_kd; - btScalar m_kp; + bool m_use_multi_dof_params; + btVector3 m_kd; + btVector3 m_kp; btScalar m_erp; btScalar m_rhsClamp; //maximum error + btVector3 m_maxAppliedImpulseMultiDof; + btVector3 m_damping; public: btMultiBodySphericalJointMotor(btMultiBody* body, int link, btScalar maxMotorImpulse); @@ -44,16 +47,32 @@ public: btMultiBodyJacobianData& data, const btContactSolverInfo& infoGlobal); - virtual void setVelocityTarget(const btVector3& velTarget, btScalar kd = 1.f) + virtual void setVelocityTarget(const btVector3& velTarget, btScalar kd = 1.0) + { + m_desiredVelocity = velTarget; + m_kd = btVector3(kd, kd, kd); + m_use_multi_dof_params = false; + } + + virtual void setVelocityTargetMultiDof(const btVector3& velTarget, const btVector3& kd = btVector3(1.0, 1.0, 1.0)) { m_desiredVelocity = velTarget; m_kd = kd; + m_use_multi_dof_params = true; } - virtual void setPositionTarget(const btQuaternion& posTarget, btScalar kp = 1.f) + virtual void setPositionTarget(const btQuaternion& posTarget, btScalar kp =1.f) + { + m_desiredPosition = posTarget; + m_kp = btVector3(kp, kp, kp); + m_use_multi_dof_params = false; + } + + virtual void setPositionTargetMultiDof(const btQuaternion& posTarget, const btVector3& kp = btVector3(1.f, 1.f, 1.f)) { m_desiredPosition = posTarget; m_kp = kp; + m_use_multi_dof_params = true; } virtual void setErp(btScalar erp) @@ -68,6 +87,28 @@ public: { m_rhsClamp = rhsClamp; } + + btScalar getMaxAppliedImpulseMultiDof(int i) const + { + return m_maxAppliedImpulseMultiDof[i]; + } + + void setMaxAppliedImpulseMultiDof(const btVector3& maxImp) + { + m_maxAppliedImpulseMultiDof = maxImp; + m_use_multi_dof_params = true; + } + + btScalar getDamping(int i) const + { + return m_damping[i]; + } + + void setDamping(const btVector3& damping) + { + m_damping = damping; + } + virtual void debugDraw(class btIDebugDraw* drawer) { //todo(erwincoumans) From 674c82db89e3f6dd6452b935f0cd1efec70f6703 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 5 Apr 2021 17:58:25 -0700 Subject: [PATCH 31/36] bump up pybullet version to 3.1.2 add missing __init__.py files --- .../minitaur/agents/trajectory_generator/__init__.py | 0 .../gym/pybullet_envs/minitaur/envs_v2/examples/__init__.py | 0 .../gym/pybullet_envs/minitaur/envs_v2/scenes/__init__.py | 0 .../gym/pybullet_envs/minitaur/envs_v2/utilities/__init__.py | 0 .../gym/pybullet_envs/minitaur/robots/safety/python/__init__.py | 0 examples/pybullet/gym/pybullet_envs/minitaur/vision/__init__.py | 0 setup.py | 2 +- 7 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/vision/__init__.py diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/trajectory_generator/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/scenes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/utilities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/vision/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/vision/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/setup.py b/setup.py index 077f697c7..84d61f329 100644 --- a/setup.py +++ b/setup.py @@ -501,7 +501,7 @@ if 'BT_USE_EGL' in EGL_CXX_FLAGS: setup( name='pybullet', - version='3.1.0', + version='3.1.2', description= 'Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning', long_description= From 513e3a7db4be2af51d9cd3bdac6c397c86b54b94 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 5 Apr 2021 18:07:39 -0700 Subject: [PATCH 32/36] add more __init__.py files --- examples/pybullet/gym/pybullet_data/configs/__init__.py | 0 examples/pybullet/gym/pybullet_data/configs_v2/__init__.py | 0 examples/pybullet/gym/pybullet_data/configs_v2/base/__init__.py | 0 examples/pybullet/gym/pybullet_data/configs_v2/robots/__init__.py | 0 examples/pybullet/gym/pybullet_data/configs_v2/scenes/__init__.py | 0 examples/pybullet/gym/pybullet_data/configs_v2/tasks/__init__.py | 0 .../pybullet/gym/pybullet_data/configs_v2/wrappers/__init__.py | 0 .../gym/pybullet_envs/minitaur/envs_v2/sensors/__init__.py | 0 8 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/pybullet/gym/pybullet_data/configs/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/base/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/robots/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/scenes/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/tasks/__init__.py create mode 100644 examples/pybullet/gym/pybullet_data/configs_v2/wrappers/__init__.py create mode 100644 examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/__init__.py diff --git a/examples/pybullet/gym/pybullet_data/configs/__init__.py b/examples/pybullet/gym/pybullet_data/configs/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/base/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/base/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/robots/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/robots/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/scenes/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/scenes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/tasks/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/tasks/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/__init__.py b/examples/pybullet/gym/pybullet_data/configs_v2/wrappers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/__init__.py new file mode 100644 index 000000000..e69de29bb From 29f388a5d8f5545a0a20a0cbe6ead7109133e071 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 5 Apr 2021 19:50:46 -0700 Subject: [PATCH 33/36] add selected examples under pybullet_examples --- .../pybullet/examples/biped2d_pybullet.py | 1 - .../TwoJointRobot_w_fixedJoints.urdf | 182 + .../TwoJointRobot_wo_fixedJoints.urdf | 110 + examples/pybullet/gym/pybullet_data/ball.vtk | 5681 ++++++++++ .../gym/pybullet_data/checker_grid.jpg | Bin 0 -> 32768 bytes .../pybullet/gym/pybullet_data/cloth_z_up.mtl | 13 + .../pybullet/gym/pybullet_data/cloth_z_up.obj | 89 + .../gym/pybullet_data/cloth_z_up.urdf | 32 + examples/pybullet/gym/pybullet_data/cube.urdf | 32 + .../pybullet_data/cube_collisionfilter.urdf | 29 + .../pybullet/gym/pybullet_data/pickup2.zip | Bin 0 -> 387045 bytes .../gym/pybullet_data/sphere_1cm.urdf | 22 + .../gym/pybullet_data/sphere_transparent.urdf | 32 + examples/pybullet/gym/pybullet_data/stone.mtl | 10 + examples/pybullet/gym/pybullet_data/stone.obj | 32 + .../gym/pybullet_data/teddy_large.urdf | 32 + .../pybullet/gym/pybullet_data/terrain.obj | 2583 +++++ .../pybullet/gym/pybullet_data/tex256.png | Bin 0 -> 12089 bytes examples/pybullet/gym/pybullet_data/torus.vtk | 9917 +++++++++++++++++ .../pybullet_data/torus/torus_textured.mtl | 11 + .../pybullet_data/torus/torus_textured.obj | 2270 ++++ .../gym/pybullet_data/torus_deform.urdf | 14 + .../gym/pybullet_data/toys/LICENSE.txt | 14 + .../gym/pybullet_data/toys/concave_box.cdf | Bin 0 -> 6285748 bytes .../gym/pybullet_data/toys/concave_box.mtl | 11 + .../gym/pybullet_data/toys/concave_box.obj | 949 ++ .../gym/pybullet_data/toys/concave_box.urdf | 30 + .../pybullet/gym/pybullet_data/toys/cube.mtl | 11 + .../pybullet/gym/pybullet_data/toys/cube.obj | 64 + .../gym/pybullet_data/toys/cylinder.mtl | 11 + .../gym/pybullet_data/toys/cylinder.obj | 282 + .../pybullet/gym/pybullet_data/toys/prism.mtl | 11 + .../pybullet/gym/pybullet_data/toys/prism.obj | 45 + .../gym/pybullet_data/toys/shape_sorter.mtl | 21 + .../gym/pybullet_data/toys/shape_sorter.obj | 400 + examples/pybullet/gym/pybullet_data/uvmap.png | Bin 0 -> 41155 bytes .../gym/pybullet_examples/__init__.py | 1 + .../gym/pybullet_examples/biped2d_pybullet.py | 39 + .../gym/pybullet_examples/collisionFilter.py | 21 + .../gym/pybullet_examples/constraint.py | 27 + .../pybullet_examples/createMultiBodyBatch.py | 143 + .../pybullet_examples/createObstacleCourse.py | 130 + .../createVisualShapeArray.py | 115 + .../pybullet_examples/deformable_anchor.py | 55 + .../gym/pybullet_examples/deformable_torus.py | 31 + .../experimentalCcdSphereRadius.py | 55 + .../gym/pybullet_examples/fileIOPlugin.py | 23 + .../gym/pybullet_examples/getClosestPoints.py | 75 + .../gym/pybullet_examples/getTextureUid.py | 21 + .../gym/pybullet_examples/heightfield.py | 138 + .../humanoidMotionCapture.py | 614 + .../gym/pybullet_examples/inverse_dynamics.py | 167 + .../pybullet_examples/inverse_kinematics.py | 125 + .../inverse_kinematics_husky_kuka.py | 202 + .../inverse_kinematics_pole.py | 66 + .../gym/pybullet_examples/jacobian.py | 102 + .../gym/pybullet_examples/manyspheres.py | 38 + .../pybullet_examples/mimicJointConstraint.py | 50 + .../gym/pybullet_examples/motorMaxVelocity.py | 22 + .../gym/pybullet_examples/pdControl.py | 152 + .../pybullet_examples/pdControllerExplicit.py | 98 + .../pybullet_examples/pdControllerStable.py | 147 + .../pointCloudFromCameraImage.py | 138 + .../gym/pybullet_examples/profileTiming.py | 25 + .../pybullet_examples/projective_texture.py | 41 + .../gym/pybullet_examples/rollPitchYaw.py | 28 + .../gym/pybullet_examples/satCollision.py | 21 + .../gym/pybullet_examples/sceneAabb.py | 42 + .../pybullet_examples/signedDistanceField.py | 17 + .../pybullet/gym/pybullet_examples/snake.py | 144 + .../gym/pybullet_examples/soccerball.py | 17 + .../switchConstraintSolver.py | 32 + .../gym/pybullet_examples/testrender_egl.py | 102 + .../gym/pybullet_examples/testrender_np.py | 90 + .../gym/pybullet_examples/transparent.py | 25 + .../pybullet/gym/pybullet_examples/vhacd.py | 13 + .../gym/pybullet_examples/video_sync_mp4.py | 32 + .../gym/pybullet_examples/vr_kuka_setup.py | 184 + setup.py | 2 +- 79 files changed, 26549 insertions(+), 2 deletions(-) create mode 100644 examples/pybullet/gym/pybullet_data/TwoJointRobot_w_fixedJoints.urdf create mode 100644 examples/pybullet/gym/pybullet_data/TwoJointRobot_wo_fixedJoints.urdf create mode 100644 examples/pybullet/gym/pybullet_data/ball.vtk create mode 100644 examples/pybullet/gym/pybullet_data/checker_grid.jpg create mode 100644 examples/pybullet/gym/pybullet_data/cloth_z_up.mtl create mode 100644 examples/pybullet/gym/pybullet_data/cloth_z_up.obj create mode 100644 examples/pybullet/gym/pybullet_data/cloth_z_up.urdf create mode 100644 examples/pybullet/gym/pybullet_data/cube.urdf create mode 100644 examples/pybullet/gym/pybullet_data/cube_collisionfilter.urdf create mode 100644 examples/pybullet/gym/pybullet_data/pickup2.zip create mode 100644 examples/pybullet/gym/pybullet_data/sphere_1cm.urdf create mode 100644 examples/pybullet/gym/pybullet_data/sphere_transparent.urdf create mode 100644 examples/pybullet/gym/pybullet_data/stone.mtl create mode 100644 examples/pybullet/gym/pybullet_data/stone.obj create mode 100644 examples/pybullet/gym/pybullet_data/teddy_large.urdf create mode 100644 examples/pybullet/gym/pybullet_data/terrain.obj create mode 100644 examples/pybullet/gym/pybullet_data/tex256.png create mode 100644 examples/pybullet/gym/pybullet_data/torus.vtk create mode 100644 examples/pybullet/gym/pybullet_data/torus/torus_textured.mtl create mode 100644 examples/pybullet/gym/pybullet_data/torus/torus_textured.obj create mode 100644 examples/pybullet/gym/pybullet_data/torus_deform.urdf create mode 100644 examples/pybullet/gym/pybullet_data/toys/LICENSE.txt create mode 100644 examples/pybullet/gym/pybullet_data/toys/concave_box.cdf create mode 100644 examples/pybullet/gym/pybullet_data/toys/concave_box.mtl create mode 100644 examples/pybullet/gym/pybullet_data/toys/concave_box.obj create mode 100644 examples/pybullet/gym/pybullet_data/toys/concave_box.urdf create mode 100644 examples/pybullet/gym/pybullet_data/toys/cube.mtl create mode 100644 examples/pybullet/gym/pybullet_data/toys/cube.obj create mode 100644 examples/pybullet/gym/pybullet_data/toys/cylinder.mtl create mode 100644 examples/pybullet/gym/pybullet_data/toys/cylinder.obj create mode 100644 examples/pybullet/gym/pybullet_data/toys/prism.mtl create mode 100644 examples/pybullet/gym/pybullet_data/toys/prism.obj create mode 100644 examples/pybullet/gym/pybullet_data/toys/shape_sorter.mtl create mode 100644 examples/pybullet/gym/pybullet_data/toys/shape_sorter.obj create mode 100644 examples/pybullet/gym/pybullet_data/uvmap.png create mode 100644 examples/pybullet/gym/pybullet_examples/__init__.py create mode 100644 examples/pybullet/gym/pybullet_examples/biped2d_pybullet.py create mode 100644 examples/pybullet/gym/pybullet_examples/collisionFilter.py create mode 100644 examples/pybullet/gym/pybullet_examples/constraint.py create mode 100644 examples/pybullet/gym/pybullet_examples/createMultiBodyBatch.py create mode 100644 examples/pybullet/gym/pybullet_examples/createObstacleCourse.py create mode 100644 examples/pybullet/gym/pybullet_examples/createVisualShapeArray.py create mode 100644 examples/pybullet/gym/pybullet_examples/deformable_anchor.py create mode 100644 examples/pybullet/gym/pybullet_examples/deformable_torus.py create mode 100644 examples/pybullet/gym/pybullet_examples/experimentalCcdSphereRadius.py create mode 100644 examples/pybullet/gym/pybullet_examples/fileIOPlugin.py create mode 100644 examples/pybullet/gym/pybullet_examples/getClosestPoints.py create mode 100644 examples/pybullet/gym/pybullet_examples/getTextureUid.py create mode 100644 examples/pybullet/gym/pybullet_examples/heightfield.py create mode 100644 examples/pybullet/gym/pybullet_examples/humanoidMotionCapture.py create mode 100644 examples/pybullet/gym/pybullet_examples/inverse_dynamics.py create mode 100644 examples/pybullet/gym/pybullet_examples/inverse_kinematics.py create mode 100644 examples/pybullet/gym/pybullet_examples/inverse_kinematics_husky_kuka.py create mode 100644 examples/pybullet/gym/pybullet_examples/inverse_kinematics_pole.py create mode 100644 examples/pybullet/gym/pybullet_examples/jacobian.py create mode 100644 examples/pybullet/gym/pybullet_examples/manyspheres.py create mode 100644 examples/pybullet/gym/pybullet_examples/mimicJointConstraint.py create mode 100644 examples/pybullet/gym/pybullet_examples/motorMaxVelocity.py create mode 100644 examples/pybullet/gym/pybullet_examples/pdControl.py create mode 100644 examples/pybullet/gym/pybullet_examples/pdControllerExplicit.py create mode 100644 examples/pybullet/gym/pybullet_examples/pdControllerStable.py create mode 100644 examples/pybullet/gym/pybullet_examples/pointCloudFromCameraImage.py create mode 100644 examples/pybullet/gym/pybullet_examples/profileTiming.py create mode 100644 examples/pybullet/gym/pybullet_examples/projective_texture.py create mode 100644 examples/pybullet/gym/pybullet_examples/rollPitchYaw.py create mode 100644 examples/pybullet/gym/pybullet_examples/satCollision.py create mode 100644 examples/pybullet/gym/pybullet_examples/sceneAabb.py create mode 100644 examples/pybullet/gym/pybullet_examples/signedDistanceField.py create mode 100644 examples/pybullet/gym/pybullet_examples/snake.py create mode 100644 examples/pybullet/gym/pybullet_examples/soccerball.py create mode 100644 examples/pybullet/gym/pybullet_examples/switchConstraintSolver.py create mode 100644 examples/pybullet/gym/pybullet_examples/testrender_egl.py create mode 100644 examples/pybullet/gym/pybullet_examples/testrender_np.py create mode 100644 examples/pybullet/gym/pybullet_examples/transparent.py create mode 100644 examples/pybullet/gym/pybullet_examples/vhacd.py create mode 100644 examples/pybullet/gym/pybullet_examples/video_sync_mp4.py create mode 100644 examples/pybullet/gym/pybullet_examples/vr_kuka_setup.py diff --git a/examples/pybullet/examples/biped2d_pybullet.py b/examples/pybullet/examples/biped2d_pybullet.py index acf7ebeed..7ddf7b976 100644 --- a/examples/pybullet/examples/biped2d_pybullet.py +++ b/examples/pybullet/examples/biped2d_pybullet.py @@ -7,7 +7,6 @@ dt = 1e-3 iters = 2000 import pybullet_data -p.setAdditionalSearchPath(pybullet_data.getDataPath()) physicsClient = p.connect(p.GUI) p.setAdditionalSearchPath(pybullet_data.getDataPath()) p.resetSimulation() diff --git a/examples/pybullet/gym/pybullet_data/TwoJointRobot_w_fixedJoints.urdf b/examples/pybullet/gym/pybullet_data/TwoJointRobot_w_fixedJoints.urdf new file mode 100644 index 000000000..4053835eb --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/TwoJointRobot_w_fixedJoints.urdf @@ -0,0 +1,182 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/pybullet_data/TwoJointRobot_wo_fixedJoints.urdf b/examples/pybullet/gym/pybullet_data/TwoJointRobot_wo_fixedJoints.urdf new file mode 100644 index 000000000..bd0dec215 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/TwoJointRobot_wo_fixedJoints.urdf @@ -0,0 +1,110 @@ + + + + + + + + + + + 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new file mode 100644 index 000000000..70d3ba1da --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/stone.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git a/examples/pybullet/gym/pybullet_data/stone.obj b/examples/pybullet/gym/pybullet_data/stone.obj new file mode 100644 index 000000000..0fbe8c5f3 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/stone.obj @@ -0,0 +1,32 @@ +# Blender v2.78 (sub 0) OBJ File: '' +# www.blender.org +mtllib stone.mtl +o Cube +v -0.246350 -0.246483 -0.000624 +v -0.151407 -0.176325 0.172867 +v -0.246350 0.249205 -0.000624 +v -0.151407 0.129477 0.172867 +v 0.249338 -0.246483 -0.000624 +v 0.154395 -0.176325 0.172867 +v 0.249338 0.249205 -0.000624 +v 0.154395 0.129477 0.172867 +vn -0.8772 0.0000 0.4801 +vn 0.0000 0.8230 0.5680 +vn 0.8772 0.0000 0.4801 +vn 0.0000 -0.9271 0.3749 +vn 0.0000 0.0000 -1.0000 +vn 0.0000 0.0000 1.0000 +usemtl None +s off +f 1//1 4//1 3//1 +f 4//2 7//2 3//2 +f 8//3 5//3 7//3 +f 6//4 1//4 5//4 +f 7//5 1//5 3//5 +f 4//6 6//6 8//6 +f 1//1 2//1 4//1 +f 4//2 8//2 7//2 +f 8//3 6//3 5//3 +f 6//4 2//4 1//4 +f 7//5 5//5 1//5 +f 4//6 2//6 6//6 diff --git a/examples/pybullet/gym/pybullet_data/teddy_large.urdf b/examples/pybullet/gym/pybullet_data/teddy_large.urdf new file mode 100644 index 000000000..2dc7d7879 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/teddy_large.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/pybullet_data/terrain.obj b/examples/pybullet/gym/pybullet_data/terrain.obj new file mode 100644 index 000000000..d8f4e204a --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/terrain.obj @@ -0,0 +1,2583 @@ +o Terrain +v -15.0 -15.0 0.0 +v -14.0 -15.0 0.0811990914301 +v -13.0 -15.0 0.0877441126682 +v -12.0 -15.0 0.0136176013718 +v -11.0 -15.0 -0.073028869825 +v -10.0 -15.0 -0.0925329348946 +v 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-0.23108457 +-0.557887793 0.227408156 0.231084719 +-0.530330241 -2.18556941e-08 -0.530329943 +-0.50226754 -0.135160238 -0.5022673010000001 +-0.50226742 -0.135160238 0.50226742 +-0.50226742 0.135160565 -0.502267122 +-0.502267241 0.135160565 0.502267241 +-0.464421362 -0.247455373 7.01256795e-08 +-0.464420974 0.247455314 7.01256155e-08 +-0.42906943 -0.247455373 -0.177726254 +-0.4296712671318722 -0.247455373 0.1747004492923097 +-0.429069072 0.247455314 -0.17772612 +-0.429069012 0.247455314 0.177726239 +-0.426989228 -0.227407992 -0.426988989 +-0.426989079 -0.227407992 0.426989079 +-0.426988959 0.227408156 -0.426988721 +-0.42698884 0.227408156 0.42698884 +-0.336284906 -0.18893747 5.07776079e-08 +-0.336284608 0.188937217 5.07775653e-08 +-0.328395605 -0.247455373 -0.328395396 +-0.328395486 -0.247455373 0.328395486 +-0.328395337 0.247455314 -0.328395128 +-0.328395218 0.247455314 0.328395218 +-0.310686767 -0.18893747 -0.128690585 +-0.310686737 -0.18893747 0.128690675 +-0.310686499 0.188937217 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0.183937415 +-0.183937415 0.07043293120000001 -0.183937311 +-0.1826027894390168 0.07043797995747503 0.1848329123509294 +-0.177726433 -0.247455373 -0.42906937 +-0.177726403 -0.247455373 0.42906937 +-0.177726284 0.247455314 -0.429069012 +-0.177726254 0.247455314 0.429069012 +-0.128690705 -0.18893747 -0.310686737 +-0.12869069 -0.18893747 0.310686737 +-0.1286906 0.188937217 -0.310686469 +-0.128690571 0.188937217 0.310686469 +-0.0995462537 -0.07043330370000001 -0.240325823 +-0.09954623880000001 -0.07043330370000001 0.240325823 +-0.0995462164 0.07043293120000001 -0.240325734 +-0.0995462015 0.07043293120000001 0.240325734 +-3.27835394e-08 -2.18556941e-08 0.75 +-3.10487849e-08 -0.135160238 0.71031338 +-0.002247339125087955 0.135160565 0.7098661234313948 +-2.6395286e-08 -0.227407992 0.603853762 +-2.639527e-08 0.227408156 0.603853405 +0.005294134150853763 -0.247455373 0.4633682886880875 +-2.03004848e-08 0.247455314 0.464420974 +-1.46994799e-08 -0.18893747 0.336284906 +-1.46994674e-08 0.188937217 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478/492/243 468/482/243 +f 394/400/197 428/438/197 392/398/197 diff --git a/examples/pybullet/gym/pybullet_data/torus_deform.urdf b/examples/pybullet/gym/pybullet_data/torus_deform.urdf new file mode 100644 index 000000000..a3b49dc94 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/torus_deform.urdf @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/pybullet_data/toys/LICENSE.txt b/examples/pybullet/gym/pybullet_data/toys/LICENSE.txt new file mode 100644 index 000000000..655f41b0f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/LICENSE.txt @@ -0,0 +1,14 @@ +URDF created by Erwin Coumans + +Bullet Continuous Collision Detection and Physics Library +http://bulletphysics.org + +This software is provided 'as-is', without any express or implied warranty. +In no event will the authors be held liable for any damages arising from the use of this software. +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it freely, +subject to the following restrictions: + +1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. +2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. +3. 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135/256/166 150/263/166 149/264/166 +f 154/265/166 155/262/166 131/225/166 +f 144/266/166 154/265/166 131/225/166 +f 135/256/166 149/264/166 151/267/166 +f 135/256/166 151/267/166 157/268/166 +f 153/269/166 144/266/166 131/225/166 +f 169/270/166 153/269/166 131/225/166 +f 131/225/166 135/256/166 159/223/166 +f 135/256/166 157/268/166 164/271/166 +f 158/224/166 169/270/166 131/225/166 +f 135/256/166 164/271/166 159/223/166 diff --git a/examples/pybullet/gym/pybullet_data/toys/concave_box.urdf b/examples/pybullet/gym/pybullet_data/toys/concave_box.urdf new file mode 100644 index 000000000..c2180ccdd --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/concave_box.urdf @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/pybullet/gym/pybullet_data/toys/cube.mtl b/examples/pybullet/gym/pybullet_data/toys/cube.mtl new file mode 100644 index 000000000..c221299b1 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/cube.mtl @@ -0,0 +1,11 @@ +# Blender MTL File: 'shape_sort.blend' +# Material Count: 1 + +newmtl Material.002 +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.017444 0.640000 0.032216 +Ks 0.034126 0.500000 0.031333 +Ni 1.000000 +d 1.000000 +illum 2 diff --git a/examples/pybullet/gym/pybullet_data/toys/cube.obj b/examples/pybullet/gym/pybullet_data/toys/cube.obj new file mode 100644 index 000000000..c2849fe05 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/cube.obj @@ -0,0 +1,64 @@ +# Blender v2.71 (sub 0) OBJ File: 'shape_sort.blend' +# www.blender.org +mtllib cube.mtl +o Cube.001_Cube.002 +v -0.231854 0.040516 -0.056463 +v -0.231854 0.040516 -0.121937 +v -0.144556 0.040516 -0.121937 +v -0.144556 0.040516 -0.056463 +v -0.231854 0.127815 -0.056463 +v -0.231854 0.127815 -0.121937 +v -0.144556 0.127815 -0.121937 +v -0.144556 0.127815 -0.056463 +v -0.231854 0.040516 -0.056463 +v -0.231854 0.040516 -0.121937 +v -0.144556 0.040516 -0.121937 +v -0.144556 0.040516 -0.056463 +v -0.231854 0.127815 -0.056463 +v -0.231854 0.127815 -0.121937 +v -0.144556 0.127815 -0.121937 +v -0.144556 0.127815 -0.056463 +vt 1.044600 0.042083 +vt 1.044600 -0.957917 +vt 0.044600 -0.957917 +vt 1.905897 0.042083 +vt 1.905897 -0.957917 +vt 0.905898 -0.957917 +vt -0.955400 0.042083 +vt -0.955400 -0.957917 +vt -0.094102 0.042083 +vt -0.094102 -0.957917 +vt 0.905898 1.044600 +vt 1.905897 1.044600 +vt 1.905897 0.044600 +vt -0.094102 1.044600 +vt -0.094102 0.044600 +vt 0.044600 0.042083 +vt 0.905898 0.042083 +vt 0.905898 0.044600 +usemtl Material.002 +s off +f 6/1 2/2 1/3 +f 7/4 3/5 2/6 +f 8/7 4/8 3/3 +f 5/9 1/10 4/6 +f 2/11 3/12 4/13 +f 7/11 6/14 5/15 +f 14/1 10/2 9/3 +f 15/4 11/5 10/6 +f 16/7 12/8 11/3 +f 13/9 9/10 12/6 +f 10/11 11/12 12/13 +f 15/11 14/14 13/15 +f 5/16 6/1 1/3 +f 6/17 7/4 2/6 +f 7/16 8/7 3/3 +f 8/17 5/9 4/6 +f 1/18 2/11 4/13 +f 8/18 7/11 5/15 +f 13/16 14/1 9/3 +f 14/17 15/4 10/6 +f 15/16 16/7 11/3 +f 16/17 13/9 12/6 +f 9/18 10/11 12/13 +f 16/18 15/11 13/15 diff --git a/examples/pybullet/gym/pybullet_data/toys/cylinder.mtl b/examples/pybullet/gym/pybullet_data/toys/cylinder.mtl new file mode 100644 index 000000000..3efd48560 --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/cylinder.mtl @@ -0,0 +1,11 @@ +# Blender MTL File: 'shape_sort.blend' +# Material Count: 1 + +newmtl Material.001 +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.013473 0.018536 0.640000 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 diff --git a/examples/pybullet/gym/pybullet_data/toys/cylinder.obj b/examples/pybullet/gym/pybullet_data/toys/cylinder.obj new file mode 100644 index 000000000..b2abf963d --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/cylinder.obj @@ -0,0 +1,282 @@ +# Blender v2.71 (sub 0) OBJ File: 'shape_sort.blend' +# www.blender.org +mtllib cylinder.mtl +o Cylinder.001 +v -0.291246 0.045696 0.165546 +v -0.214241 0.045696 0.165546 +v -0.291246 0.034429 0.166100 +v -0.214241 0.034429 0.166100 +v -0.291246 0.023595 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0.510414 +vt 0.020864 0.507658 +vt 0.163092 0.476412 +vt 0.008587 0.505209 +vt 0.133302 0.473963 +vt 1.008587 0.505209 +vt 0.996728 0.503160 +vt 1.106812 0.471914 +vt 0.984865 0.501590 +vt 1.082707 0.470344 +vt 0.972628 0.500559 +vt 0.959652 0.500107 +vt 1.036012 0.468861 +vt 0.945535 0.500252 +vt 1.008811 0.469006 +vt 0.929810 0.500987 +vt 0.972352 0.469741 +vt 0.911918 0.502285 +vt 0.913273 0.471038 +vt 0.891203 0.504095 +vt 0.866959 0.506348 +vt 0.698338 0.475102 +vt 0.838607 0.508958 +vt 0.630535 0.477711 +vt 0.806049 0.511823 +vt 0.592448 0.480577 +vt 0.770128 0.514835 +vt 0.732794 0.517877 +vt 0.549219 0.486631 +vt 0.696491 0.520833 +vt 0.534123 0.489586 +vt 0.663092 0.523588 +vt 0.520864 0.492342 +vt 0.633302 0.526037 +vt 0.508586 0.494791 +vt 0.606812 0.528086 +vt 0.496728 0.496840 +vt 0.582707 0.529656 +vt 0.559695 0.530687 +vt 0.472628 0.499441 +vt 0.536012 0.531139 +vt 0.459652 0.499893 +vt 0.508810 0.530994 +vt 0.445535 0.499748 +vt 0.472352 0.530259 +vt 0.413273 0.528962 +vt 0.411918 0.497715 +vt 0.310810 0.527152 +vt 0.391202 0.495905 +vt 0.198337 0.524898 +vt 1.020864 0.507658 +vt 1.034123 0.510414 +vt 0.130535 0.522289 +vt 0.366959 0.493652 +vt 1.391202 0.495905 +vt 0.196491 0.479167 +vt 1.133302 0.473963 +vt 1.059695 0.469313 +vt 0.810811 0.472848 +vt 0.567655 0.483589 +vt 0.484865 0.498410 +vt 0.429810 0.499013 +vt 1.092448 0.519423 +vt 1.067655 0.516411 +vt 1.198337 0.524898 +vt 1.413273 0.528962 +vt 1.310810 0.527152 +vt 1.472352 0.530259 +vt 1.130535 0.522289 +vt 1.049219 0.513369 +vt 0.338607 0.491042 +vt 1.270128 0.485165 +vt 1.306049 0.488177 +vt 1.196491 0.479167 +vt 1.232794 0.482123 +vt 1.163092 0.476412 +vt 1.366959 0.493652 +vt 1.338607 0.491042 +vt 1.429810 0.499013 +vt 1.411918 0.497715 +vt 1.445535 0.499748 +vt 1.459652 0.499893 +usemtl Material.001 +s off +f 1/1 2/2 4/3 +f 3/4 4/3 6/5 +f 5/6 6/5 8/7 +f 8/7 10/8 9/9 +f 10/8 12/10 11/11 +f 12/12 14/13 13/14 +f 14/13 16/15 15/16 +f 15/16 16/15 18/17 +f 18/17 20/18 19/19 +f 20/18 22/20 21/21 +f 22/20 24/22 23/23 +f 24/22 26/24 25/25 +f 25/25 26/24 28/26 +f 28/26 30/27 29/28 +f 30/27 32/29 31/30 +f 32/29 34/31 33/32 +f 33/32 34/31 36/33 +f 36/33 38/34 37/35 +f 38/34 40/36 39/37 +f 40/36 42/38 41/39 +f 42/38 44/40 43/41 +f 44/40 46/42 45/43 +f 45/43 46/42 48/44 +f 48/44 50/45 49/46 +f 50/45 52/47 51/48 +f 52/47 54/49 53/50 +f 53/50 54/49 56/51 +f 56/51 58/52 57/53 +f 58/52 60/54 59/55 +f 59/55 60/54 62/56 +f 26/24 10/57 8/58 +f 64/59 2/2 1/1 +f 61/60 62/56 64/59 +f 37/35 39/37 59/61 +f 3/4 1/1 4/3 +f 5/6 3/4 6/5 +f 7/62 5/6 8/7 +f 7/62 8/7 9/9 +f 9/9 10/8 11/11 +f 11/63 12/12 13/14 +f 13/14 14/13 15/16 +f 17/64 15/16 18/17 +f 17/64 18/17 19/19 +f 19/19 20/18 21/21 +f 21/21 22/20 23/23 +f 23/23 24/22 25/25 +f 27/65 25/25 28/26 +f 27/65 28/26 29/28 +f 29/28 30/27 31/30 +f 31/30 32/29 33/32 +f 35/66 33/32 36/33 +f 35/66 36/33 37/35 +f 37/35 38/34 39/37 +f 39/37 40/36 41/39 +f 41/39 42/38 43/41 +f 43/41 44/40 45/43 +f 47/67 45/43 48/44 +f 47/67 48/44 49/46 +f 49/46 50/45 51/48 +f 51/48 52/47 53/50 +f 55/68 53/50 56/51 +f 55/68 56/51 57/53 +f 57/53 58/52 59/55 +f 61/60 59/55 62/56 +f 2/69 34/31 4/70 +f 38/34 36/33 62/71 +f 58/72 40/36 60/73 +f 54/49 44/40 56/74 +f 50/45 48/44 52/47 +f 46/42 54/49 48/44 +f 14/13 22/20 20/18 +f 12/12 10/57 24/22 +f 2/69 64/75 34/31 +f 30/27 6/76 32/29 +f 26/24 8/58 28/26 +f 63/77 61/60 64/59 +f 64/75 36/33 34/31 +f 14/13 20/18 16/15 +f 44/40 42/38 56/74 +f 40/36 38/34 60/73 +f 38/34 62/71 60/73 +f 10/57 26/24 24/22 +f 48/44 54/49 52/47 +f 34/31 32/29 4/70 +f 8/58 6/76 28/26 +f 54/49 46/42 44/40 +f 20/18 18/17 16/15 +f 42/38 58/72 56/74 +f 63/77 64/59 1/1 +f 32/29 6/76 4/70 +f 12/12 24/22 14/13 +f 58/72 42/38 40/36 +f 36/33 64/75 62/71 +f 24/22 22/20 14/13 +f 6/76 30/27 28/26 +f 3/78 31/30 1/79 +f 7/80 29/28 5/81 +f 11/63 23/23 9/82 +f 15/16 21/21 13/14 +f 15/16 17/64 19/19 +f 61/83 63/84 35/66 +f 27/65 7/80 25/25 +f 41/39 55/85 57/86 +f 35/66 63/84 33/32 +f 53/87 55/85 43/41 +f 43/41 55/85 41/39 +f 47/67 53/87 45/43 +f 47/67 49/46 51/88 +f 59/61 39/37 57/86 +f 29/28 31/30 5/81 +f 7/80 27/65 29/28 +f 45/43 53/87 43/41 +f 47/67 51/88 53/87 +f 61/83 37/35 59/61 +f 31/30 3/78 5/81 +f 15/16 19/19 21/21 +f 21/21 23/23 13/14 +f 39/37 41/39 57/86 +f 7/80 9/82 25/25 +f 1/79 31/30 33/32 +f 23/23 11/63 13/14 +f 9/82 23/23 25/25 +f 37/35 61/83 35/66 +f 63/84 1/79 33/32 diff --git a/examples/pybullet/gym/pybullet_data/toys/prism.mtl b/examples/pybullet/gym/pybullet_data/toys/prism.mtl new file mode 100644 index 000000000..ed3e8a1ae --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/prism.mtl @@ -0,0 +1,11 @@ +# Blender MTL File: 'shape_sort.blend' +# Material Count: 1 + +newmtl Material.003 +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.640000 0.007339 0.006282 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 diff --git a/examples/pybullet/gym/pybullet_data/toys/prism.obj b/examples/pybullet/gym/pybullet_data/toys/prism.obj new file mode 100644 index 000000000..b8b0d6f0f --- /dev/null +++ b/examples/pybullet/gym/pybullet_data/toys/prism.obj @@ -0,0 +1,45 @@ +# Blender v2.71 (sub 0) OBJ File: 'shape_sort.blend' +# www.blender.org +mtllib prism.mtl +o Cube.002_Cube.005 +v -0.233641 -0.103557 0.060897 +v -0.233641 -0.103557 -0.057063 +v -0.149383 -0.103557 -0.057063 +v -0.149383 -0.103557 0.060897 +v -0.233013 -0.039217 0.035115 +v -0.233013 -0.039217 -0.031280 +v -0.150011 -0.039217 -0.031280 +v -0.150011 -0.039217 0.035115 +vt 0.780473 0.523151 +vt 0.999041 -0.022288 +vt -0.000959 -0.022288 +vt 1.896793 0.523151 +vt 1.904244 -0.022288 +vt 0.904244 -0.022288 +vt 0.217610 0.523151 +vt -0.088305 0.523151 +vt -0.095756 -0.022288 +vt 0.904244 1.999041 +vt 1.904244 1.999041 +vt 1.904244 0.999041 +vt 0.896793 0.780473 +vt -0.088305 0.780473 +vt -0.088305 0.217610 +vt 0.911695 0.523151 +vt 0.896793 0.523151 +vt 0.904244 0.999041 +vt 0.896793 0.217610 +usemtl Material.003 +s off +f 6/1 2/2 1/3 +f 7/4 3/5 2/6 +f 8/7 4/3 3/2 +f 5/8 1/9 4/6 +f 2/10 3/11 4/12 +f 7/13 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znt=~qo;PjfkF=0J^-V0(>~6~%xjb=8-;Moxf?9$`uNf0OzP!Hyst{^^ zx3!#|ofNd-ljKATBwQHLgb-O?M+cqGSOE?gs;j0XGn(`$5x8!OSlnWoQ|760)& zV3QAXCDka2v_`@q#&`_K*?j+g%U=ox^yfgH(lFYsji4B3zsw_vG{30icYny8bPDz! zO!IXr03l7>C9B#TxH>fLlK-ELRY2Mbti;uj@vIDzwSG$e>?^p8^qx9Y8}EL( z^V|C{Oo{=oFY3}Kp6DhNT;v+Ph}gMzKwOUAl_^);%zK9Q*x}ixu}6jf8*TxJ5~J8{ z*lr=x z08+aJS7tNKGM>j4+MxzF5pb=y+bE)4XvRgF7D%Ssv8rJ|fy3s%Da_FxWR9uuSgt{% zXr|6($lVSY;*yTeso6em7d9+SRTkH6bzei+TZ2JeO{+r{FF&56n*T-Y{)@k@ z6}L7EspQmC{RAF$tH|beZGxqLk!kBlPtsV1Vl-BCOMD^L4QU&0KraBxYC7!zcyH&h z&C9!+5*7HVu|Y*^hyhI(zCuoT_a2AR$W%=zbkk#}ZM546?*m0(YBbqAy3xQDvFD9% zv*7(b7|=7^qY9T(tBJb+rt&;h*~q zDU_?B^?&}~fYadtA%U0w8=$35(NWjZchS-{)TO|m{~K`VT`C math.pi): + a = -math.pi + time.sleep(.01) + p.setGravity(0, 0, -10) + pivot = [a, 0, 1] + orn = p.getQuaternionFromEuler([a, 0, 0]) + p.changeConstraint(cid, pivot, jointChildFrameOrientation=orn, maxForce=50) + +p.removeConstraint(cid) diff --git a/examples/pybullet/gym/pybullet_examples/createMultiBodyBatch.py b/examples/pybullet/gym/pybullet_examples/createMultiBodyBatch.py new file mode 100644 index 000000000..87fde9c84 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/createMultiBodyBatch.py @@ -0,0 +1,143 @@ +import pybullet as p +import time +import math +import pybullet_data +cid = p.connect(p.SHARED_MEMORY) +if (cid < 0): + p.connect(p.GUI, options="--minGraphicsUpdateTimeMs=16000") +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +p.setPhysicsEngineParameter(numSolverIterations=4, minimumSolverIslandSize=1024) +p.setTimeStep(1. / 120.) +logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "createMultiBodyBatch.json") +#useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone) +p.loadURDF("plane100.urdf", useMaximalCoordinates=True) +#disable rendering during creation. +p.setPhysicsEngineParameter(contactBreakingThreshold=0.04) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) +#disable tinyrenderer, software (CPU) renderer, we don't use it here +p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0) + +shift = [0, -0.02, 0] +meshScale = [0.1, 0.1, 0.1] + +vertices = [[-1.000000, -1.000000, 1.000000], [1.000000, -1.000000, 1.000000], + [1.000000, 1.000000, 1.000000], [-1.000000, 1.000000, 1.000000], + [-1.000000, -1.000000, -1.000000], [1.000000, -1.000000, -1.000000], + [1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, -1.000000], + [-1.000000, -1.000000, -1.000000], [-1.000000, 1.000000, -1.000000], + [-1.000000, 1.000000, 1.000000], [-1.000000, -1.000000, 1.000000], + [1.000000, -1.000000, -1.000000], [1.000000, 1.000000, -1.000000], + [1.000000, 1.000000, 1.000000], [1.000000, -1.000000, 1.000000], + [-1.000000, -1.000000, -1.000000], [-1.000000, -1.000000, 1.000000], + [1.000000, -1.000000, 1.000000], [1.000000, -1.000000, -1.000000], + [-1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, 1.000000], + [1.000000, 1.000000, 1.000000], [1.000000, 1.000000, -1.000000]] + +normals = [[0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000], + [0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000], + [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000], + [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000], + [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000], + [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000], + [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000], + [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000], + [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000], + [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000], + [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000], + [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000]] + +uvs = [[0.750000, 0.250000], [1.000000, 0.250000], [1.000000, 0.000000], [0.750000, 0.000000], + [0.500000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000], [0.500000, 0.000000], + [0.500000, 0.000000], [0.750000, 0.000000], [0.750000, 0.250000], [0.500000, 0.250000], + [0.250000, 0.500000], [0.250000, 0.250000], [0.000000, 0.250000], [0.000000, 0.500000], + [0.250000, 0.500000], [0.250000, 0.250000], [0.500000, 0.250000], [0.500000, 0.500000], + [0.000000, 0.000000], [0.000000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000]] +indices = [ + 0, + 1, + 2, + 0, + 2, + 3, #//ground face + 6, + 5, + 4, + 7, + 6, + 4, #//top face + 10, + 9, + 8, + 11, + 10, + 8, + 12, + 13, + 14, + 12, + 14, + 15, + 18, + 17, + 16, + 19, + 18, + 16, + 20, + 21, + 22, + 20, + 22, + 23 +] + +#p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0) +#the visual shape and collision shape can be re-used by all createMultiBody instances (instancing) +visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH, + rgbaColor=[1, 1, 1, 1], + specularColor=[0.4, .4, 0], + visualFramePosition=shift, + meshScale=meshScale, + vertices=vertices, + indices=indices, + uvs=uvs, + normals=normals) +collisionShapeId = p.createCollisionShape( + shapeType=p.GEOM_BOX, halfExtents=meshScale +) #MESH, vertices=vertices, collisionFramePosition=shift,meshScale=meshScale) + +texUid = p.loadTexture("tex256.png") + +batchPositions = [] + +for x in range(32): + for y in range(32): + for z in range(10): + batchPositions.append( + [x * meshScale[0] * 5.5, y * meshScale[1] * 5.5, (0.5 + z) * meshScale[2] * 2.5]) + +bodyUids = p.createMultiBody(baseMass=0, + baseInertialFramePosition=[0, 0, 0], + baseCollisionShapeIndex=collisionShapeId, + baseVisualShapeIndex=visualShapeId, + basePosition=[0, 0, 2], + batchPositions=batchPositions, + useMaximalCoordinates=True) +p.changeVisualShape(bodyUids[0], -1, textureUniqueId=texUid) + +p.syncBodyInfo() +print("numBodies=", p.getNumBodies()) +p.stopStateLogging(logId) +p.setGravity(0, 0, -10) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) + +colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]] +currentColor = 0 + +while (1): + p.stepSimulation() + #time.sleep(1./120.) + #p.getCameraImage(320,200) diff --git a/examples/pybullet/gym/pybullet_examples/createObstacleCourse.py b/examples/pybullet/gym/pybullet_examples/createObstacleCourse.py new file mode 100644 index 000000000..a9ab11908 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/createObstacleCourse.py @@ -0,0 +1,130 @@ +import pybullet as p +import time +import math +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +#don't create a ground plane, to allow for gaps etc +p.resetSimulation() +#p.createCollisionShape(p.GEOM_PLANE) +#p.createMultiBody(0,0) +#p.resetDebugVisualizerCamera(5,75,-26,[0,0,1]); +p.resetDebugVisualizerCamera(15, -346, -16, [-15, 0, 1]) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + +sphereRadius = 0.05 +colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius) + +#a few different ways to create a mesh: + +#convex mesh from obj +stoneId = p.createCollisionShape(p.GEOM_MESH, fileName="stone.obj") + +boxHalfLength = 0.5 +boxHalfWidth = 2.5 +boxHalfHeight = 0.1 +segmentLength = 5 + +colBoxId = p.createCollisionShape(p.GEOM_BOX, + halfExtents=[boxHalfLength, boxHalfWidth, boxHalfHeight]) + +mass = 1 +visualShapeId = -1 + +segmentStart = 0 + +for i in range(segmentLength): + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=colBoxId, + basePosition=[segmentStart, 0, -0.1]) + segmentStart = segmentStart - 1 + +for i in range(segmentLength): + height = 0 + if (i % 2): + height = 1 + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=colBoxId, + basePosition=[segmentStart, 0, -0.1 + height]) + segmentStart = segmentStart - 1 + +baseOrientation = p.getQuaternionFromEuler([math.pi / 2., 0, math.pi / 2.]) + +for i in range(segmentLength): + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=colBoxId, + basePosition=[segmentStart, 0, -0.1]) + segmentStart = segmentStart - 1 + if (i % 2): + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=colBoxId, + basePosition=[segmentStart, i % 3, -0.1 + height + boxHalfWidth], + baseOrientation=baseOrientation) + +for i in range(segmentLength): + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=colBoxId, + basePosition=[segmentStart, 0, -0.1]) + width = 4 + for j in range(width): + p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=stoneId, + basePosition=[segmentStart, 0.5 * (i % 2) + j - width / 2., 0]) + segmentStart = segmentStart - 1 + +link_Masses = [1] +linkCollisionShapeIndices = [colBoxId] +linkVisualShapeIndices = [-1] +linkPositions = [[0, 0, 0]] +linkOrientations = [[0, 0, 0, 1]] +linkInertialFramePositions = [[0, 0, 0]] +linkInertialFrameOrientations = [[0, 0, 0, 1]] +indices = [0] +jointTypes = [p.JOINT_REVOLUTE] +axis = [[1, 0, 0]] + +baseOrientation = [0, 0, 0, 1] +for i in range(segmentLength): + boxId = p.createMultiBody(0, + colSphereId, + -1, [segmentStart, 0, -0.1], + baseOrientation, + linkMasses=link_Masses, + linkCollisionShapeIndices=linkCollisionShapeIndices, + linkVisualShapeIndices=linkVisualShapeIndices, + linkPositions=linkPositions, + linkOrientations=linkOrientations, + linkInertialFramePositions=linkInertialFramePositions, + linkInertialFrameOrientations=linkInertialFrameOrientations, + linkParentIndices=indices, + linkJointTypes=jointTypes, + linkJointAxis=axis) + p.changeDynamics(boxId, -1, spinningFriction=0.001, rollingFriction=0.001, linearDamping=0.0) + print(p.getNumJoints(boxId)) + for joint in range(p.getNumJoints(boxId)): + targetVelocity = 10 + if (i % 2): + targetVelocity = -10 + p.setJointMotorControl2(boxId, + joint, + p.VELOCITY_CONTROL, + targetVelocity=targetVelocity, + force=100) + segmentStart = segmentStart - 1.1 + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) +while (1): + camData = p.getDebugVisualizerCamera() + viewMat = camData[2] + projMat = camData[3] + p.getCameraImage(256, + 256, + viewMatrix=viewMat, + projectionMatrix=projMat, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + keys = p.getKeyboardEvents() + p.stepSimulation() + #print(keys) + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/createVisualShapeArray.py b/examples/pybullet/gym/pybullet_examples/createVisualShapeArray.py new file mode 100644 index 000000000..6156d808c --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/createVisualShapeArray.py @@ -0,0 +1,115 @@ +import pybullet as p +import time +import math +import pybullet_data + + +def getRayFromTo(mouseX, mouseY): + width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera( + ) + camPos = [ + camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1], + camTarget[2] - dist * camForward[2] + ] + farPlane = 10000 + rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])] + invLen = farPlane * 1. / (math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] * + rayForward[1] + rayForward[2] * rayForward[2])) + rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]] + rayFrom = camPos + oneOverWidth = float(1) / float(width) + oneOverHeight = float(1) / float(height) + dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth] + dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight] + rayToCenter = [ + rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2] + ] + rayTo = [ + rayFrom[0] + rayForward[0] - 0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] - + float(mouseY) * dVer[0], rayFrom[1] + rayForward[1] - 0.5 * horizon[1] + 0.5 * vertical[1] + + float(mouseX) * dHor[1] - float(mouseY) * dVer[1], rayFrom[2] + rayForward[2] - + 0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2] + ] + return rayFrom, rayTo + + +cid = p.connect(p.SHARED_MEMORY) +if (cid < 0): + p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setPhysicsEngineParameter(numSolverIterations=10) +p.setTimeStep(1. / 120.) +logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "visualShapeBench.json") +#useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone) +p.loadURDF("plane100.urdf", useMaximalCoordinates=True) +#disable rendering during creation. +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) +#disable tinyrenderer, software (CPU) renderer, we don't use it here +p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0) + +shift = [0, -0.02, 0] +shift1 = [0, 0.1, 0] +shift2 = [0, 0, 0] + +meshScale = [0.1, 0.1, 0.1] +#the visual shape and collision shape can be re-used by all createMultiBody instances (instancing) +visualShapeId = p.createVisualShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX], + halfExtents=[[0, 0, 0], [0.1, 0.1, 0.1]], + fileNames=["duck.obj", ""], + visualFramePositions=[ + shift1, + shift2, + ], + meshScales=[meshScale, meshScale]) +collisionShapeId = p.createCollisionShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX], + halfExtents=[[0, 0, 0], [0.1, 0.1, 0.1]], + fileNames=["duck_vhacd.obj", ""], + collisionFramePositions=[ + shift1, + shift2, + ], + meshScales=[meshScale, meshScale]) + +rangex = 2 +rangey = 2 +for i in range(rangex): + for j in range(rangey): + mb = p.createMultiBody(baseMass=1, + baseInertialFramePosition=[0, 0, 0], + baseCollisionShapeIndex=collisionShapeId, + baseVisualShapeIndex=visualShapeId, + basePosition=[((-rangex / 2) + i * 2) * meshScale[0] * 2, + (-rangey / 2 + j) * meshScale[1] * 4, 1], + useMaximalCoordinates=False) + p.changeVisualShape(mb, -1, rgbaColor=[1, 1, 1, 1]) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) +p.stopStateLogging(logId) +p.setGravity(0, 0, -10) +p.setRealTimeSimulation(1) + +colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]] +currentColor = 0 + +p.getCameraImage(64, 64, renderer=p.ER_BULLET_HARDWARE_OPENGL) + +while (1): + + mouseEvents = p.getMouseEvents() + for e in mouseEvents: + if ((e[0] == 2) and (e[3] == 0) and (e[4] & p.KEY_WAS_TRIGGERED)): + mouseX = e[1] + mouseY = e[2] + rayFrom, rayTo = getRayFromTo(mouseX, mouseY) + rayInfo = p.rayTest(rayFrom, rayTo) + #p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3) + for l in range(len(rayInfo)): + hit = rayInfo[l] + objectUid = hit[0] + if (objectUid >= 0): + #p.removeBody(objectUid) + p.changeVisualShape(objectUid, -1, rgbaColor=colors[currentColor]) + currentColor += 1 + if (currentColor >= len(colors)): + currentColor = 0 diff --git a/examples/pybullet/gym/pybullet_examples/deformable_anchor.py b/examples/pybullet/gym/pybullet_examples/deformable_anchor.py new file mode 100644 index 000000000..bf37cdb61 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/deformable_anchor.py @@ -0,0 +1,55 @@ +import pybullet as p +from time import sleep + +physicsClient = p.connect(p.GUI) +import pybullet_data + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.resetSimulation(p.RESET_USE_DEFORMABLE_WORLD) + +gravZ=-10 +p.setGravity(0, 0, gravZ) + +planeOrn = [0,0,0,1]#p.getQuaternionFromEuler([0.3,0,0]) +#planeId = p.loadURDF("plane.urdf", [0,0,-2],planeOrn) + +boxId = p.loadURDF("cube.urdf", [0,1,2],useMaximalCoordinates = True) + +clothId = p.loadSoftBody("cloth_z_up.obj", basePosition = [0,0,2], scale = 0.5, mass = 1., useNeoHookean = 0, useBendingSprings=1,useMassSpring=1, springElasticStiffness=40, springDampingStiffness=.1, springDampingAllDirections = 1, useSelfCollision = 0, frictionCoeff = .5, useFaceContact=1) + + +p.changeVisualShape(clothId, -1, flags=p.VISUAL_SHAPE_DOUBLE_SIDED) + +p.createSoftBodyAnchor(clothId ,24,-1,-1) +p.createSoftBodyAnchor(clothId ,20,-1,-1) +p.createSoftBodyAnchor(clothId ,15,boxId,-1, [0.5,-0.5,0]) +p.createSoftBodyAnchor(clothId ,19,boxId,-1, [-0.5,-0.5,0]) +p.setPhysicsEngineParameter(sparseSdfVoxelSize=0.25) + +debug = True +if debug: + data = p.getMeshData(clothId, -1, flags=p.MESH_DATA_SIMULATION_MESH) + print("--------------") + print("data=",data) + print(data[0]) + print(data[1]) + text_uid = [] + for i in range(data[0]): + pos = data[1][i] + uid = p.addUserDebugText(str(i), pos, textColorRGB=[1,1,1]) + text_uid.append(uid) + +while p.isConnected(): + p.getCameraImage(320,200) + + if debug: + data = p.getMeshData(clothId, -1, flags=p.MESH_DATA_SIMULATION_MESH) + for i in range(data[0]): + pos = data[1][i] + uid = p.addUserDebugText(str(i), pos, textColorRGB=[1,1,1], replaceItemUniqueId=text_uid[i]) + + p.setGravity(0,0,gravZ) + p.stepSimulation() + #p.configureDebugVisualizer(p.COV_ENABLE_SINGLE_STEP_RENDERING,1) + #sleep(1./240.) + diff --git a/examples/pybullet/gym/pybullet_examples/deformable_torus.py b/examples/pybullet/gym/pybullet_examples/deformable_torus.py new file mode 100644 index 000000000..b8d7cb052 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/deformable_torus.py @@ -0,0 +1,31 @@ +import pybullet as p +from time import sleep +import pybullet_data + +physicsClient = p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +p.resetSimulation(p.RESET_USE_DEFORMABLE_WORLD) +p.resetDebugVisualizerCamera(3,-420,-30,[0.3,0.9,-2]) +p.setGravity(0, 0, -10) + +tex = p.loadTexture("uvmap.png") +planeId = p.loadURDF("plane.urdf", [0,0,-2]) + +boxId = p.loadURDF("cube.urdf", [0,3,2],useMaximalCoordinates = True) + +bunnyId = p.loadSoftBody("torus/torus_textured.obj", simFileName="torus.vtk", mass = 3, useNeoHookean = 1, NeoHookeanMu = 180, NeoHookeanLambda = 600, NeoHookeanDamping = 0.01, collisionMargin = 0.006, useSelfCollision = 1, frictionCoeff = 0.5, repulsionStiffness = 800) +p.changeVisualShape(bunnyId, -1, rgbaColor=[1,1,1,1], textureUniqueId=tex, flags=0) + + +bunny2 = p.loadURDF("torus_deform.urdf", [0,1,0.2], flags=p.URDF_USE_SELF_COLLISION) + +p.changeVisualShape(bunny2, -1, rgbaColor=[1,1,1,1], textureUniqueId=tex, flags=0) +p.setPhysicsEngineParameter(sparseSdfVoxelSize=0.25) +p.setRealTimeSimulation(0) + +while p.isConnected(): + p.stepSimulation() + p.getCameraImage(320,200) + p.setGravity(0,0,-10) diff --git a/examples/pybullet/gym/pybullet_examples/experimentalCcdSphereRadius.py b/examples/pybullet/gym/pybullet_examples/experimentalCcdSphereRadius.py new file mode 100644 index 000000000..ca7dd664a --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/experimentalCcdSphereRadius.py @@ -0,0 +1,55 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setPhysicsEngineParameter(allowedCcdPenetration=0.0) + +terrain_mass = 0 +terrain_visual_shape_id = -1 +terrain_position = [0, 0, 0] +terrain_orientation = [0, 0, 0, 1] +terrain_collision_shape_id = p.createCollisionShape(shapeType=p.GEOM_MESH, + fileName="terrain.obj", + flags=p.GEOM_FORCE_CONCAVE_TRIMESH | + p.GEOM_CONCAVE_INTERNAL_EDGE, + meshScale=[0.5, 0.5, 0.5]) +p.createMultiBody(terrain_mass, terrain_collision_shape_id, terrain_visual_shape_id, + terrain_position, terrain_orientation) + +useMaximalCoordinates = True +sphereRadius = 0.005 +colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius) +colBoxId = p.createCollisionShape(p.GEOM_BOX, + halfExtents=[sphereRadius, sphereRadius, sphereRadius]) + +mass = 1 +visualShapeId = -1 + +for i in range(5): + for j in range(5): + for k in range(5): + #if (k&2): + sphereUid = p.createMultiBody( + mass, + colSphereId, + visualShapeId, [-i * 5 * sphereRadius, j * 5 * sphereRadius, k * 2 * sphereRadius + 1], + useMaximalCoordinates=useMaximalCoordinates) + #else: + # sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,[-i*2*sphereRadius,j*2*sphereRadius,k*2*sphereRadius+1], useMaximalCoordinates=useMaximalCoordinates) + p.changeDynamics(sphereUid, + -1, + spinningFriction=0.001, + rollingFriction=0.001, + linearDamping=0.0) + p.changeDynamics(sphereUid, -1, ccdSweptSphereRadius=0.002) + +p.setGravity(0, 0, -10) + +pts = p.getContactPoints() +print("num points=", len(pts)) +print(pts) +while (p.isConnected()): + time.sleep(1. / 240.) + p.stepSimulation() diff --git a/examples/pybullet/gym/pybullet_examples/fileIOPlugin.py b/examples/pybullet/gym/pybullet_examples/fileIOPlugin.py new file mode 100644 index 000000000..07f906ec0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/fileIOPlugin.py @@ -0,0 +1,23 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +fileIO = p.loadPlugin("fileIOPlugin") +if (fileIO >= 0): + #we can have a zipfile (pickup.zip) inside a zipfile (pickup2.zip) + p.executePluginCommand(fileIO, pybullet_data.getDataPath()+"/pickup2.zip", [p.AddFileIOAction, p.ZipFileIO]) + p.executePluginCommand(fileIO, "pickup.zip", [p.AddFileIOAction, p.ZipFileIO]) + objs = p.loadSDF("pickup/model.sdf") + dobot = objs[0] + p.changeVisualShape(dobot, -1, rgbaColor=[1, 1, 1, 1]) + +else: + print("fileIOPlugin is disabled.") + +p.setPhysicsEngineParameter(enableFileCaching=False) + +while (1): + p.stepSimulation() + time.sleep(1. / 240.) diff --git a/examples/pybullet/gym/pybullet_examples/getClosestPoints.py b/examples/pybullet/gym/pybullet_examples/getClosestPoints.py new file mode 100644 index 000000000..55311f58d --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/getClosestPoints.py @@ -0,0 +1,75 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +useCollisionShapeQuery = True +p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) +geom = p.createCollisionShape(p.GEOM_SPHERE, radius=0.1) +geomBox = p.createCollisionShape(p.GEOM_BOX, halfExtents=[0.2, 0.2, 0.2]) +baseOrientationB = p.getQuaternionFromEuler([0, 0.3, 0]) #[0,0.5,0.5,0] +basePositionB = [1.5, 0, 1] +obA = -1 +obB = -1 + +obA = p.createMultiBody(baseMass=0, baseCollisionShapeIndex=geom, basePosition=[0.5, 0, 1]) +obB = p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=geomBox, + basePosition=basePositionB, + baseOrientation=baseOrientationB) + +lineWidth = 3 +colorRGB = [1, 0, 0] +lineId = p.addUserDebugLine(lineFromXYZ=[0, 0, 0], + lineToXYZ=[0, 0, 0], + lineColorRGB=colorRGB, + lineWidth=lineWidth, + lifeTime=0) +pitch = 0 +yaw = 0 + +while (p.isConnected()): + pitch += 0.01 + if (pitch >= 3.1415 * 2.): + pitch = 0 + yaw += 0.01 + if (yaw >= 3.1415 * 2.): + yaw = 0 + + baseOrientationB = p.getQuaternionFromEuler([yaw, pitch, 0]) + if (obB >= 0): + p.resetBasePositionAndOrientation(obB, basePositionB, baseOrientationB) + + if (useCollisionShapeQuery): + pts = p.getClosestPoints(bodyA=-1, + bodyB=-1, + distance=100, + collisionShapeA=geom, + collisionShapeB=geomBox, + collisionShapePositionA=[0.5, 0, 1], + collisionShapePositionB=basePositionB, + collisionShapeOrientationB=baseOrientationB) + #pts = p.getClosestPoints(bodyA=obA, bodyB=-1, distance=100, collisionShapeB=geomBox, collisionShapePositionB=basePositionB, collisionShapeOrientationB=baseOrientationB) + else: + pts = p.getClosestPoints(bodyA=obA, bodyB=obB, distance=100) + + if len(pts) > 0: + #print(pts) + distance = pts[0][8] + #print("distance=",distance) + ptA = pts[0][5] + ptB = pts[0][6] + p.addUserDebugLine(lineFromXYZ=ptA, + lineToXYZ=ptB, + lineColorRGB=colorRGB, + lineWidth=lineWidth, + lifeTime=0, + replaceItemUniqueId=lineId) + #time.sleep(1./240.) + +#removeCollisionShape is optional: +#only use removeCollisionShape if the collision shape is not used to create a body +#and if you want to keep on creating new collision shapes for different queries (not recommended) +p.removeCollisionShape(geom) +p.removeCollisionShape(geomBox) diff --git a/examples/pybullet/gym/pybullet_examples/getTextureUid.py b/examples/pybullet/gym/pybullet_examples/getTextureUid.py new file mode 100644 index 000000000..f4a31b2f0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/getTextureUid.py @@ -0,0 +1,21 @@ +import pybullet as p +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +plane = p.loadURDF("plane.urdf") +visualData = p.getVisualShapeData(plane, p.VISUAL_SHAPE_DATA_TEXTURE_UNIQUE_IDS) +print(visualData) +curTexUid = visualData[0][8] +print(curTexUid) +texUid = p.loadTexture("tex256.png") +print("texUid=", texUid) + +p.changeVisualShape(plane, -1, textureUniqueId=texUid) + +for i in range(100): + p.getCameraImage(320, 200) +p.changeVisualShape(plane, -1, textureUniqueId=curTexUid) + +for i in range(100): + p.getCameraImage(320, 200) diff --git a/examples/pybullet/gym/pybullet_examples/heightfield.py b/examples/pybullet/gym/pybullet_examples/heightfield.py new file mode 100644 index 000000000..b01a5e0bb --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/heightfield.py @@ -0,0 +1,138 @@ +import pybullet as p +import pybullet_data as pd +import math +import time + +p.connect(p.GUI) +p.setAdditionalSearchPath(pd.getDataPath()) + +textureId = -1 + +useProgrammatic = 0 +useTerrainFromPNG = 1 +useDeepLocoCSV = 2 +updateHeightfield = False + +heightfieldSource = useProgrammatic +import random +random.seed(10) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0) +heightPerturbationRange = 0.05 +if heightfieldSource==useProgrammatic: + numHeightfieldRows = 256 + numHeightfieldColumns = 256 + heightfieldData = [0]*numHeightfieldRows*numHeightfieldColumns + for j in range (int(numHeightfieldColumns/2)): + for i in range (int(numHeightfieldRows/2) ): + height = random.uniform(0,heightPerturbationRange) + heightfieldData[2*i+2*j*numHeightfieldRows]=height + heightfieldData[2*i+1+2*j*numHeightfieldRows]=height + heightfieldData[2*i+(2*j+1)*numHeightfieldRows]=height + heightfieldData[2*i+1+(2*j+1)*numHeightfieldRows]=height + + + terrainShape = p.createCollisionShape(shapeType = p.GEOM_HEIGHTFIELD, meshScale=[.05,.05,1], heightfieldTextureScaling=(numHeightfieldRows-1)/2, heightfieldData=heightfieldData, numHeightfieldRows=numHeightfieldRows, numHeightfieldColumns=numHeightfieldColumns) + terrain = p.createMultiBody(0, terrainShape) + p.resetBasePositionAndOrientation(terrain,[0,0,0], [0,0,0,1]) + +if heightfieldSource==useDeepLocoCSV: + terrainShape = p.createCollisionShape(shapeType = p.GEOM_HEIGHTFIELD, meshScale=[.5,.5,2.5],fileName = "heightmaps/ground0.txt", heightfieldTextureScaling=128) + terrain = p.createMultiBody(0, terrainShape) + p.resetBasePositionAndOrientation(terrain,[0,0,0], [0,0,0,1]) + +if heightfieldSource==useTerrainFromPNG: + terrainShape = p.createCollisionShape(shapeType = p.GEOM_HEIGHTFIELD, meshScale=[.1,.1,24],fileName = "heightmaps/wm_height_out.png") + textureId = p.loadTexture("heightmaps/gimp_overlay_out.png") + terrain = p.createMultiBody(0, terrainShape) + p.changeVisualShape(terrain, -1, textureUniqueId = textureId) + + +p.changeVisualShape(terrain, -1, rgbaColor=[1,1,1,1]) + + +sphereRadius = 0.05 +colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius) +colBoxId = p.createCollisionShape(p.GEOM_BOX, + halfExtents=[sphereRadius, sphereRadius, sphereRadius]) + +mass = 1 +visualShapeId = -1 + +link_Masses = [1] +linkCollisionShapeIndices = [colBoxId] +linkVisualShapeIndices = [-1] +linkPositions = [[0, 0, 0.11]] +linkOrientations = [[0, 0, 0, 1]] +linkInertialFramePositions = [[0, 0, 0]] +linkInertialFrameOrientations = [[0, 0, 0, 1]] +indices = [0] +jointTypes = [p.JOINT_REVOLUTE] +axis = [[0, 0, 1]] + +for i in range(3): + for j in range(3): + for k in range(3): + basePosition = [ + i * 5 * sphereRadius, j * 5 * sphereRadius, 1 + k * 5 * sphereRadius + 1 + ] + baseOrientation = [0, 0, 0, 1] + if (k & 2): + sphereUid = p.createMultiBody(mass, colSphereId, visualShapeId, basePosition, + baseOrientation) + else: + sphereUid = p.createMultiBody(mass, + colBoxId, + visualShapeId, + basePosition, + baseOrientation, + linkMasses=link_Masses, + linkCollisionShapeIndices=linkCollisionShapeIndices, + linkVisualShapeIndices=linkVisualShapeIndices, + linkPositions=linkPositions, + linkOrientations=linkOrientations, + linkInertialFramePositions=linkInertialFramePositions, + linkInertialFrameOrientations=linkInertialFrameOrientations, + linkParentIndices=indices, + linkJointTypes=jointTypes, + linkJointAxis=axis) + + + p.changeDynamics(sphereUid, + -1, + spinningFriction=0.001, + rollingFriction=0.001, + linearDamping=0.0) + for joint in range(p.getNumJoints(sphereUid)): + p.setJointMotorControl2(sphereUid, joint, p.VELOCITY_CONTROL, targetVelocity=1, force=10) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1) +p.setGravity(0, 0, -10) +p.setRealTimeSimulation(1) + +p.getNumJoints(sphereUid) +for i in range(p.getNumJoints(sphereUid)): + p.getJointInfo(sphereUid, i) + + +while (p.isConnected()): + keys = p.getKeyboardEvents() + + if updateHeightfield and heightfieldSource==useProgrammatic: + for j in range (int(numHeightfieldColumns/2)): + for i in range (int(numHeightfieldRows/2) ): + height = random.uniform(0,heightPerturbationRange)#+math.sin(time.time()) + heightfieldData[2*i+2*j*numHeightfieldRows]=height + heightfieldData[2*i+1+2*j*numHeightfieldRows]=height + heightfieldData[2*i+(2*j+1)*numHeightfieldRows]=height + heightfieldData[2*i+1+(2*j+1)*numHeightfieldRows]=height + #GEOM_CONCAVE_INTERNAL_EDGE may help avoid getting stuck at an internal (shared) edge of the triangle/heightfield. + #GEOM_CONCAVE_INTERNAL_EDGE is a bit slower to build though. + #flags = p.GEOM_CONCAVE_INTERNAL_EDGE + flags = 0 + terrainShape2 = p.createCollisionShape(shapeType = p.GEOM_HEIGHTFIELD, flags = flags, meshScale=[.05,.05,1], heightfieldTextureScaling=(numHeightfieldRows-1)/2, heightfieldData=heightfieldData, numHeightfieldRows=numHeightfieldRows, numHeightfieldColumns=numHeightfieldColumns, replaceHeightfieldIndex = terrainShape) + + + #print(keys) + #getCameraImage note: software/TinyRenderer doesn't render/support heightfields! + #p.getCameraImage(320,200, renderer=p.ER_BULLET_HARDWARE_OPENGL) + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/humanoidMotionCapture.py b/examples/pybullet/gym/pybullet_examples/humanoidMotionCapture.py new file mode 100644 index 000000000..fdc1675e1 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/humanoidMotionCapture.py @@ -0,0 +1,614 @@ +import pybullet as p +import json +import time +import pybullet_data + + +useGUI = True +if useGUI: + p.connect(p.GUI) +else: + p.connect(p.DIRECT) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +useZUp = False +useYUp = not useZUp +showJointMotorTorques = False + +if useYUp: + p.configureDebugVisualizer(p.COV_ENABLE_Y_AXIS_UP, 1) + +from pybullet_examples.pdControllerExplicit import PDControllerExplicitMultiDof +from pybullet_examples.pdControllerStable import PDControllerStableMultiDof + +explicitPD = PDControllerExplicitMultiDof(p) +stablePD = PDControllerStableMultiDof(p) + +p.resetDebugVisualizerCamera(cameraDistance=7.4, + cameraYaw=-94, + cameraPitch=-14, + cameraTargetPosition=[0.24, -0.02, -0.09]) + +import pybullet_data +p.setTimeOut(10000) +useMotionCapture = False +useMotionCaptureReset = False #not useMotionCapture +useExplicitPD = True + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setPhysicsEngineParameter(numSolverIterations=30) +#p.setPhysicsEngineParameter(solverResidualThreshold=1e-30) + +#explicit PD control requires small timestep +timeStep = 1. / 600. +#timeStep = 1./240. + +p.setPhysicsEngineParameter(fixedTimeStep=timeStep) + +path = pybullet_data.getDataPath() + "/data/motions/humanoid3d_backflip.txt" +#path = pybullet_data.getDataPath()+"/data/motions/humanoid3d_cartwheel.txt" +#path = pybullet_data.getDataPath()+"/data/motions/humanoid3d_walk.txt" + +#p.loadURDF("plane.urdf",[0,0,-1.03]) +print("path = ", path) +with open(path, 'r') as f: + motion_dict = json.load(f) +#print("motion_dict = ", motion_dict) +print("len motion=", len(motion_dict)) +print(motion_dict['Loop']) +numFrames = len(motion_dict['Frames']) +print("#frames = ", numFrames) + +frameId = p.addUserDebugParameter("frame", 0, numFrames - 1, 0) + +erpId = p.addUserDebugParameter("erp", 0, 1, 0.2) + +kpMotorId = p.addUserDebugParameter("kpMotor", 0, 1, .2) +forceMotorId = p.addUserDebugParameter("forceMotor", 0, 2000, 1000) + +jointTypes = [ + "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED" +] + +startLocations = [[0, 0, 2], [0, 0, 0], [0, 0, -2], [0, 0, -4], [0, 0, 4]] + +p.addUserDebugText("Stable PD", + [startLocations[0][0], startLocations[0][1] + 1, startLocations[0][2]], + [0, 0, 0]) +p.addUserDebugText("Spherical Drive", + [startLocations[1][0], startLocations[1][1] + 1, startLocations[1][2]], + [0, 0, 0]) +p.addUserDebugText("Explicit PD", + [startLocations[2][0], startLocations[2][1] + 1, startLocations[2][2]], + [0, 0, 0]) +p.addUserDebugText("Kinematic", + [startLocations[3][0], startLocations[3][1] + 1, startLocations[3][2]], + [0, 0, 0]) +p.addUserDebugText("Stable PD (Py)", + [startLocations[4][0], startLocations[4][1] + 1, startLocations[4][2]], + [0, 0, 0]) +flags=p.URDF_MAINTAIN_LINK_ORDER+p.URDF_USE_SELF_COLLISION +humanoid = p.loadURDF("humanoid/humanoid.urdf", + startLocations[0], + globalScaling=0.25, + useFixedBase=False, + flags=flags) +humanoid2 = p.loadURDF("humanoid/humanoid.urdf", + startLocations[1], + globalScaling=0.25, + useFixedBase=False, + flags=flags) +humanoid3 = p.loadURDF("humanoid/humanoid.urdf", + startLocations[2], + globalScaling=0.25, + useFixedBase=False, + flags=flags) +humanoid4 = p.loadURDF("humanoid/humanoid.urdf", + startLocations[3], + globalScaling=0.25, + useFixedBase=False, + flags=flags) +humanoid5 = p.loadURDF("humanoid/humanoid.urdf", + startLocations[4], + globalScaling=0.25, + useFixedBase=False, + flags=flags) + +humanoid_fix = p.createConstraint(humanoid, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], + startLocations[0], [0, 0, 0, 1]) +humanoid2_fix = p.createConstraint(humanoid2, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], + startLocations[1], [0, 0, 0, 1]) +humanoid3_fix = p.createConstraint(humanoid3, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], + startLocations[2], [0, 0, 0, 1]) +humanoid3_fix = p.createConstraint(humanoid4, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], + startLocations[3], [0, 0, 0, 1]) +humanoid4_fix = p.createConstraint(humanoid5, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], + startLocations[4], [0, 0, 0, 1]) + +startPose = [ + 2, 0.847532, 0, 0.9986781045, 0.01410400148, -0.0006980000731, -0.04942300517, 0.9988133229, + 0.009485003066, -0.04756001538, -0.004475001447, 1, 0, 0, 0, 0.9649395871, 0.02436898957, + -0.05755497537, 0.2549218909, -0.249116, 0.9993661511, 0.009952001505, 0.03265400494, + 0.01009800153, 0.9854981188, -0.06440700776, 0.09324301124, -0.1262970152, 0.170571, + 0.9927545808, -0.02090099117, 0.08882396249, -0.07817796699, -0.391532, 0.9828788495, + 0.1013909845, -0.05515999155, 0.143618978, 0.9659421276, 0.1884590249, -0.1422460188, + 0.105854014, 0.581348 +] + +startVel = [ + 1.235314324, -0.008525509087, 0.1515293946, -1.161516553, 0.1866449799, -0.1050802848, 0, + 0.935706195, 0.08277326387, 0.3002461862, 0, 0, 0, 0, 0, 1.114409628, 0.3618553952, + -0.4505575061, 0, -1.725374735, -0.5052852598, -0.8555179722, -0.2221173515, 0, -0.1837617357, + 0.00171895706, 0.03912837591, 0, 0.147945294, 1.837653345, 0.1534535548, 1.491385941, 0, + -4.632454387, -0.9111172777, -1.300648184, -1.345694622, 0, -1.084238535, 0.1313680236, + -0.7236998534, 0, -0.5278312973 +] + +p.resetBasePositionAndOrientation(humanoid, startLocations[0], [0, 0, 0, 1]) +p.resetBasePositionAndOrientation(humanoid2, startLocations[1], [0, 0, 0, 1]) +p.resetBasePositionAndOrientation(humanoid3, startLocations[2], [0, 0, 0, 1]) +p.resetBasePositionAndOrientation(humanoid4, startLocations[3], [0, 0, 0, 1]) +p.resetBasePositionAndOrientation(humanoid5, startLocations[4], [0, 0, 0, 1]) + +index0 = 7 +for j in range(p.getNumJoints(humanoid)): + ji = p.getJointInfo(humanoid, j) + targetPosition = [0] + jointType = ji[2] + if (jointType == p.JOINT_SPHERICAL): + targetPosition = [ + startPose[index0 + 1], startPose[index0 + 2], startPose[index0 + 3], startPose[index0 + 0] + ] + targetVel = [startVel[index0 + 0], startVel[index0 + 1], startVel[index0 + 2]] + index0 += 4 + print("spherical position: ", targetPosition) + print("spherical velocity: ", targetVel) + p.resetJointStateMultiDof(humanoid, j, targetValue=targetPosition, targetVelocity=targetVel) + p.resetJointStateMultiDof(humanoid5, j, targetValue=targetPosition, targetVelocity=targetVel) + p.resetJointStateMultiDof(humanoid2, j, targetValue=targetPosition, targetVelocity=targetVel) + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + targetPosition = [startPose[index0]] + targetVel = [startVel[index0]] + index0 += 1 + print("revolute:", targetPosition) + print("revolute velocity:", targetVel) + p.resetJointStateMultiDof(humanoid, j, targetValue=targetPosition, targetVelocity=targetVel) + p.resetJointStateMultiDof(humanoid5, j, targetValue=targetPosition, targetVelocity=targetVel) + p.resetJointStateMultiDof(humanoid2, j, targetValue=targetPosition, targetVelocity=targetVel) + +for j in range(p.getNumJoints(humanoid)): + ji = p.getJointInfo(humanoid, j) + targetPosition = [0] + jointType = ji[2] + if (jointType == p.JOINT_SPHERICAL): + targetPosition = [0, 0, 0, 1] + p.setJointMotorControlMultiDof(humanoid, + j, + p.POSITION_CONTROL, + targetPosition, + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[0, 0, 0]) + p.setJointMotorControlMultiDof(humanoid5, + j, + p.POSITION_CONTROL, + targetPosition, + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[0, 0, 0]) + p.setJointMotorControlMultiDof(humanoid3, + j, + p.POSITION_CONTROL, + targetPosition, + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[31, 31, 31]) + p.setJointMotorControlMultiDof(humanoid4, + j, + p.POSITION_CONTROL, + targetPosition, + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[1, 1, 1]) + + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + p.setJointMotorControl2(humanoid, j, p.VELOCITY_CONTROL, targetVelocity=0, force=0) + p.setJointMotorControl2(humanoid3, j, p.VELOCITY_CONTROL, targetVelocity=0, force=31) + p.setJointMotorControl2(humanoid4, j, p.VELOCITY_CONTROL, targetVelocity=0, force=10) + p.setJointMotorControl2(humanoid5, j, p.VELOCITY_CONTROL, targetVelocity=0, force=0) + + #print(ji) + print("joint[", j, "].type=", jointTypes[ji[2]]) + print("joint[", j, "].name=", ji[1]) + +jointIds = [] +paramIds = [] +for j in range(p.getNumJoints(humanoid)): + #p.changeDynamics(humanoid,j,linearDamping=0, angularDamping=0) + p.changeVisualShape(humanoid, j, rgbaColor=[1, 1, 1, 1]) + info = p.getJointInfo(humanoid, j) + #print(info) + if (not useMotionCapture): + jointName = info[1] + jointType = info[2] + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + jointIds.append(j) + #paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,0)) + #print("jointName=",jointName, "at ", j) + +p.changeVisualShape(humanoid, 2, rgbaColor=[1, 0, 0, 1]) +chest = 1 +neck = 2 +rightHip = 3 +rightKnee = 4 +rightAnkle = 5 +rightShoulder = 6 +rightElbow = 7 +leftHip = 9 +leftKnee = 10 +leftAnkle = 11 +leftShoulder = 12 +leftElbow = 13 + +#rightShoulder=3 +#rightElbow=4 +#leftShoulder=6 +#leftElbow = 7 +#rightHip = 9 +#rightKnee=10 +#rightAnkle=11 +#leftHip = 12 +#leftKnee=13 +#leftAnkle=14 + +import time + +kpOrg = [ + 0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500, 400, + 400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400, 400, 400, + 400, 400, 300 +] +kdOrg = [ + 0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40, 40, + 40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30 +] + +once = True +p.getCameraImage(320, 200) + +while (p.isConnected()): + + if useGUI: + erp = p.readUserDebugParameter(erpId) + kpMotor = p.readUserDebugParameter(kpMotorId) + maxForce = p.readUserDebugParameter(forceMotorId) + frameReal = p.readUserDebugParameter(frameId) + else: + erp = 0.2 + kpMotor = 0.2 + maxForce = 1000 + frameReal = 0 + + kp = kpMotor + + frame = int(frameReal) + frameNext = frame + 1 + if (frameNext >= numFrames): + frameNext = frame + + frameFraction = frameReal - frame + #print("frameFraction=",frameFraction) + #print("frame=",frame) + #print("frameNext=", frameNext) + + #getQuaternionSlerp + + frameData = motion_dict['Frames'][frame] + frameDataNext = motion_dict['Frames'][frameNext] + + #print("duration=",frameData[0]) + #print(pos=[frameData]) + + basePos1Start = [frameData[1], frameData[2], frameData[3]] + basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + basePos1 = [ + basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]), + basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]), + basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2]) + ] + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] + baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]] + baseOrn1 = p.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction) + #pre-rotate to make z-up + if (useZUp): + y2zPos = [0, 0, 0.0] + y2zOrn = p.getQuaternionFromEuler([1.57, 0, 0]) + basePos, baseOrn = p.multiplyTransforms(y2zPos, y2zOrn, basePos1, baseOrn1) + p.resetBasePositionAndOrientation(humanoid, basePos, baseOrn) + + y2zPos = [0, 2, 0.0] + y2zOrn = p.getQuaternionFromEuler([1.57, 0, 0]) + basePos, baseOrn = p.multiplyTransforms(y2zPos, y2zOrn, basePos1, baseOrn1) + p.resetBasePositionAndOrientation(humanoid2, basePos, baseOrn) + + chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]] + chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]] + chestRot = p.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction) + + neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]] + neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]] + neckRot = p.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction) + + rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]] + rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]] + rightHipRot = p.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd, frameFraction) + + rightKneeRotStart = [frameData[20]] + rightKneeRotEnd = [frameDataNext[20]] + rightKneeRot = [ + rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0]) + ] + + rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]] + rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]] + rightAnkleRot = p.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd, frameFraction) + + rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]] + rightShoulderRotEnd = [ + frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25] + ] + rightShoulderRot = p.getQuaternionSlerp(rightShoulderRotStart, rightShoulderRotEnd, + frameFraction) + + rightElbowRotStart = [frameData[29]] + rightElbowRotEnd = [frameDataNext[29]] + rightElbowRot = [ + rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0]) + ] + + leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]] + leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]] + leftHipRot = p.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd, frameFraction) + + leftKneeRotStart = [frameData[34]] + leftKneeRotEnd = [frameDataNext[34]] + leftKneeRot = [leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0])] + + leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]] + leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]] + leftAnkleRot = p.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd, frameFraction) + + leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]] + leftShoulderRotEnd = [frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39]] + leftShoulderRot = p.getQuaternionSlerp(leftShoulderRotStart, leftShoulderRotEnd, frameFraction) + leftElbowRotStart = [frameData[43]] + leftElbowRotEnd = [frameDataNext[43]] + leftElbowRot = [ + leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0]) + ] + + if (0): #if (once): + p.resetJointStateMultiDof(humanoid, chest, chestRot) + p.resetJointStateMultiDof(humanoid, neck, neckRot) + p.resetJointStateMultiDof(humanoid, rightHip, rightHipRot) + p.resetJointStateMultiDof(humanoid, rightKnee, rightKneeRot) + p.resetJointStateMultiDof(humanoid, rightAnkle, rightAnkleRot) + p.resetJointStateMultiDof(humanoid, rightShoulder, rightShoulderRot) + p.resetJointStateMultiDof(humanoid, rightElbow, rightElbowRot) + p.resetJointStateMultiDof(humanoid, leftHip, leftHipRot) + p.resetJointStateMultiDof(humanoid, leftKnee, leftKneeRot) + p.resetJointStateMultiDof(humanoid, leftAnkle, leftAnkleRot) + p.resetJointStateMultiDof(humanoid, leftShoulder, leftShoulderRot) + p.resetJointStateMultiDof(humanoid, leftElbow, leftElbowRot) + once = False + #print("chestRot=",chestRot) + p.setGravity(0, 0, -10) + + kp = kpMotor + if (useExplicitPD): + jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1] + #[x,y,z] base position and [x,y,z,w] base orientation! + totalDofs = 7 + for dof in jointDofCounts: + totalDofs += dof + + jointIndicesAll = [ + chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee, + leftAnkle, leftShoulder, leftElbow + ] + basePos, baseOrn = p.getBasePositionAndOrientation(humanoid) + pose = [ + basePos[0], basePos[1], basePos[2], baseOrn[0], baseOrn[1], baseOrn[2], baseOrn[3], + chestRot[0], chestRot[1], chestRot[2], chestRot[3], neckRot[0], neckRot[1], neckRot[2], + neckRot[3], rightHipRot[0], rightHipRot[1], rightHipRot[2], rightHipRot[3], + rightKneeRot[0], rightAnkleRot[0], rightAnkleRot[1], rightAnkleRot[2], rightAnkleRot[3], + rightShoulderRot[0], rightShoulderRot[1], rightShoulderRot[2], rightShoulderRot[3], + rightElbowRot[0], leftHipRot[0], leftHipRot[1], leftHipRot[2], leftHipRot[3], + leftKneeRot[0], leftAnkleRot[0], leftAnkleRot[1], leftAnkleRot[2], leftAnkleRot[3], + leftShoulderRot[0], leftShoulderRot[1], leftShoulderRot[2], leftShoulderRot[3], + leftElbowRot[0] + ] + + #print("pose=") + #for po in pose: + # print(po) + + + taus = stablePD.computePD(bodyUniqueId=humanoid5, + jointIndices=jointIndicesAll, + desiredPositions=pose, + desiredVelocities=[0] * totalDofs, + kps=kpOrg, + kds=kdOrg, + maxForces=[maxForce] * totalDofs, + timeStep=timeStep) + + indices = [chest, neck, rightHip, rightKnee, + rightAnkle, rightShoulder, rightElbow, + leftHip, leftKnee, leftAnkle, + leftShoulder, leftElbow] + targetPositions = [chestRot,neckRot,rightHipRot, rightKneeRot, + rightAnkleRot, rightShoulderRot, rightElbowRot, + leftHipRot, leftKneeRot, leftAnkleRot, + leftShoulderRot, leftElbowRot] + maxForces = [ [maxForce,maxForce,maxForce], [maxForce,maxForce,maxForce],[maxForce,maxForce,maxForce],[maxForce], + [maxForce,maxForce,maxForce],[maxForce,maxForce,maxForce],[maxForce], + [maxForce,maxForce,maxForce], [maxForce], [maxForce,maxForce,maxForce], + [maxForce,maxForce,maxForce], [maxForce]] + + + kps = [1000]*12 + kds = [100]*12 + + + p.setJointMotorControlMultiDofArray(humanoid, + indices, + p.STABLE_PD_CONTROL, + targetPositions=targetPositions, + positionGains=kps, + velocityGains=kds, + forces=maxForces) + + taus3 = explicitPD.computePD(bodyUniqueId=humanoid3, + jointIndices=jointIndicesAll, + desiredPositions=pose, + desiredVelocities=[0] * totalDofs, + kps=kpOrg, + kds=kdOrg, + maxForces=[maxForce * 0.05] * totalDofs, + timeStep=timeStep) + + #taus=[0]*43 + dofIndex = 7 + for index in range(len(jointIndicesAll)): + jointIndex = jointIndicesAll[index] + if jointDofCounts[index] == 4: + + p.setJointMotorControlMultiDof( + humanoid5, + jointIndex, + p.TORQUE_CONTROL, + force=[taus[dofIndex + 0], taus[dofIndex + 1], taus[dofIndex + 2]]) + p.setJointMotorControlMultiDof( + humanoid3, + jointIndex, + p.TORQUE_CONTROL, + force=[taus3[dofIndex + 0], taus3[dofIndex + 1], taus3[dofIndex + 2]]) + + if jointDofCounts[index] == 1: + + + p.setJointMotorControlMultiDof(humanoid5, + jointIndex, + controlMode=p.TORQUE_CONTROL, + force=[taus[dofIndex]]) + p.setJointMotorControlMultiDof(humanoid3, + jointIndex, + controlMode=p.TORQUE_CONTROL, + force=[taus3[dofIndex]]) + + dofIndex += jointDofCounts[index] + + #print("len(taus)=",len(taus)) + #print("taus=",taus) + + p.setJointMotorControlMultiDof(humanoid2, + chest, + p.POSITION_CONTROL, + targetPosition=chestRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + neck, + p.POSITION_CONTROL, + targetPosition=neckRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + rightHip, + p.POSITION_CONTROL, + targetPosition=rightHipRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + rightKnee, + p.POSITION_CONTROL, + targetPosition=rightKneeRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + rightAnkle, + p.POSITION_CONTROL, + targetPosition=rightAnkleRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + rightShoulder, + p.POSITION_CONTROL, + targetPosition=rightShoulderRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + rightElbow, + p.POSITION_CONTROL, + targetPosition=rightElbowRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + leftHip, + p.POSITION_CONTROL, + targetPosition=leftHipRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + leftKnee, + p.POSITION_CONTROL, + targetPosition=leftKneeRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + leftAnkle, + p.POSITION_CONTROL, + targetPosition=leftAnkleRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + leftShoulder, + p.POSITION_CONTROL, + targetPosition=leftShoulderRot, + positionGain=kp, + force=[maxForce]) + p.setJointMotorControlMultiDof(humanoid2, + leftElbow, + p.POSITION_CONTROL, + targetPosition=leftElbowRot, + positionGain=kp, + force=[maxForce]) + + kinematicHumanoid4 = True + if (kinematicHumanoid4): + p.resetJointStateMultiDof(humanoid4, chest, chestRot) + p.resetJointStateMultiDof(humanoid4, neck, neckRot) + p.resetJointStateMultiDof(humanoid4, rightHip, rightHipRot) + p.resetJointStateMultiDof(humanoid4, rightKnee, rightKneeRot) + p.resetJointStateMultiDof(humanoid4, rightAnkle, rightAnkleRot) + p.resetJointStateMultiDof(humanoid4, rightShoulder, rightShoulderRot) + p.resetJointStateMultiDof(humanoid4, rightElbow, rightElbowRot) + p.resetJointStateMultiDof(humanoid4, leftHip, leftHipRot) + p.resetJointStateMultiDof(humanoid4, leftKnee, leftKneeRot) + p.resetJointStateMultiDof(humanoid4, leftAnkle, leftAnkleRot) + p.resetJointStateMultiDof(humanoid4, leftShoulder, leftShoulderRot) + p.resetJointStateMultiDof(humanoid4, leftElbow, leftElbowRot) + p.stepSimulation() + + if showJointMotorTorques: + for j in range(p.getNumJoints(humanoid2)): + jointState = p.getJointStateMultiDof(humanoid2, j) + print("jointStateMultiDof[", j, "].pos=", jointState[0]) + print("jointStateMultiDof[", j, "].vel=", jointState[1]) + print("jointStateMultiDof[", j, "].jointForces=", jointState[3]) + time.sleep(timeStep) diff --git a/examples/pybullet/gym/pybullet_examples/inverse_dynamics.py b/examples/pybullet/gym/pybullet_examples/inverse_dynamics.py new file mode 100644 index 000000000..5ec99aef8 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/inverse_dynamics.py @@ -0,0 +1,167 @@ +import pybullet as bullet +import pybullet_data as pd + +plot = True +import time + +if (plot): + import matplotlib.pyplot as plt +import math +verbose = False + +# Parameters: +robot_base = [0., 0., 0.] +robot_orientation = [0., 0., 0., 1.] +delta_t = 0.0001 + +# Initialize Bullet Simulator +id_simulator = bullet.connect(bullet.GUI) # or bullet.DIRECT for non-graphical version +bullet.setTimeStep(delta_t) +bullet.setAdditionalSearchPath(pd.getDataPath()) + +# Switch between URDF with/without FIXED joints +with_fixed_joints = True + +if with_fixed_joints: + id_revolute_joints = [0, 3] + id_robot = bullet.loadURDF("TwoJointRobot_w_fixedJoints.urdf", + robot_base, + robot_orientation, + useFixedBase=True) +else: + id_revolute_joints = [0, 1] + id_robot = bullet.loadURDF("TwoJointRobot_wo_fixedJoints.urdf", + robot_base, + robot_orientation, + useFixedBase=True) + +bullet.changeDynamics(id_robot, -1, linearDamping=0, angularDamping=0) +bullet.changeDynamics(id_robot, 0, linearDamping=0, angularDamping=0) +bullet.changeDynamics(id_robot, 1, linearDamping=0, angularDamping=0) + +jointTypeNames = [ + "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED", + "JOINT_POINT2POINT", "JOINT_GEAR" +] + +# Disable the motors for torque control: +bullet.setJointMotorControlArray(id_robot, + id_revolute_joints, + bullet.VELOCITY_CONTROL, + forces=[0.0, 0.0]) + +# Target Positions: +start = 0.0 +end = 1.0 + +steps = int((end - start) / delta_t) +t = [0] * steps +q_pos_desired = [[0.] * steps, [0.] * steps] +q_vel_desired = [[0.] * steps, [0.] * steps] +q_acc_desired = [[0.] * steps, [0.] * steps] + +for s in range(steps): + t[s] = start + s * delta_t + q_pos_desired[0][s] = 1. / (2. * math.pi) * math.sin(2. * math.pi * t[s]) - t[s] + q_pos_desired[1][s] = -1. / (2. * math.pi) * (math.cos(2. * math.pi * t[s]) - 1.0) + + q_vel_desired[0][s] = math.cos(2. * math.pi * t[s]) - 1. + q_vel_desired[1][s] = math.sin(2. * math.pi * t[s]) + + q_acc_desired[0][s] = -2. * math.pi * math.sin(2. * math.pi * t[s]) + q_acc_desired[1][s] = 2. * math.pi * math.cos(2. * math.pi * t[s]) + +q_pos = [[0.] * steps, [0.] * steps] +q_vel = [[0.] * steps, [0.] * steps] +q_tor = [[0.] * steps, [0.] * steps] + +# Do Torque Control: +for i in range(len(t)): + + # Read Sensor States: + joint_states = bullet.getJointStates(id_robot, id_revolute_joints) + + q_pos[0][i] = joint_states[0][0] + a = joint_states[1][0] + if (verbose): + print("joint_states[1][0]") + print(joint_states[1][0]) + q_pos[1][i] = a + + q_vel[0][i] = joint_states[0][1] + q_vel[1][i] = joint_states[1][1] + + # Computing the torque from inverse dynamics: + obj_pos = [q_pos[0][i], q_pos[1][i]] + obj_vel = [q_vel[0][i], q_vel[1][i]] + obj_acc = [q_acc_desired[0][i], q_acc_desired[1][i]] + + if (verbose): + print("calculateInverseDynamics") + print("id_robot") + print(id_robot) + print("obj_pos") + print(obj_pos) + print("obj_vel") + print(obj_vel) + print("obj_acc") + print(obj_acc) + + torque = bullet.calculateInverseDynamics(id_robot, obj_pos, obj_vel, obj_acc) + q_tor[0][i] = torque[0] + q_tor[1][i] = torque[1] + if (verbose): + print("torque=") + print(torque) + + # Set the Joint Torques: + bullet.setJointMotorControlArray(id_robot, + id_revolute_joints, + bullet.TORQUE_CONTROL, + forces=[torque[0], torque[1]]) + + # Step Simulation + bullet.stepSimulation() + +# Plot the Position, Velocity and Acceleration: +if plot: + figure = plt.figure(figsize=[15, 4.5]) + figure.subplots_adjust(left=0.05, bottom=0.11, right=0.97, top=0.9, wspace=0.4, hspace=0.55) + + ax_pos = figure.add_subplot(141) + ax_pos.set_title("Joint Position") + ax_pos.plot(t, q_pos_desired[0], '--r', lw=4, label='Desired q0') + ax_pos.plot(t, q_pos_desired[1], '--b', lw=4, label='Desired q1') + ax_pos.plot(t, q_pos[0], '-r', lw=1, label='Measured q0') + ax_pos.plot(t, q_pos[1], '-b', lw=1, label='Measured q1') + ax_pos.set_ylim(-1., 1.) + ax_pos.legend() + + ax_vel = figure.add_subplot(142) + ax_vel.set_title("Joint Velocity") + ax_vel.plot(t, q_vel_desired[0], '--r', lw=4, label='Desired q0') + ax_vel.plot(t, q_vel_desired[1], '--b', lw=4, label='Desired q1') + ax_vel.plot(t, q_vel[0], '-r', lw=1, label='Measured q0') + ax_vel.plot(t, q_vel[1], '-b', lw=1, label='Measured q1') + ax_vel.set_ylim(-2., 2.) + ax_vel.legend() + + ax_acc = figure.add_subplot(143) + ax_acc.set_title("Joint Acceleration") + ax_acc.plot(t, q_acc_desired[0], '--r', lw=4, label='Desired q0') + ax_acc.plot(t, q_acc_desired[1], '--b', lw=4, label='Desired q1') + ax_acc.set_ylim(-10., 10.) + ax_acc.legend() + + ax_tor = figure.add_subplot(144) + ax_tor.set_title("Executed Torque") + ax_tor.plot(t, q_tor[0], '-r', lw=2, label='Torque q0') + ax_tor.plot(t, q_tor[1], '-b', lw=2, label='Torque q1') + ax_tor.set_ylim(-20., 20.) + ax_tor.legend() + + plt.pause(0.01) + +while (1): + bullet.stepSimulation() + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/inverse_kinematics.py b/examples/pybullet/gym/pybullet_examples/inverse_kinematics.py new file mode 100644 index 000000000..bf198ff32 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/inverse_kinematics.py @@ -0,0 +1,125 @@ +import pybullet as p +import time +import math +from datetime import datetime +import pybullet_data + +clid = p.connect(p.SHARED_MEMORY) +if (clid < 0): + p.connect(p.GUI) + #p.connect(p.SHARED_MEMORY_GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +p.loadURDF("plane.urdf", [0, 0, -0.3]) +kukaId = p.loadURDF("kuka_iiwa/model.urdf", [0, 0, 0]) +p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1]) +kukaEndEffectorIndex = 6 +numJoints = p.getNumJoints(kukaId) +if (numJoints != 7): + exit() + +#lower limits for null space +ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] +#upper limits for null space +ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] +#joint ranges for null space +jr = [5.8, 4, 5.8, 4, 5.8, 4, 6] +#restposes for null space +rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] +#joint damping coefficents +jd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + +for i in range(numJoints): + p.resetJointState(kukaId, i, rp[i]) + +p.setGravity(0, 0, 0) +t = 0. +prevPose = [0, 0, 0] +prevPose1 = [0, 0, 0] +hasPrevPose = 0 +useNullSpace = 1 + +useOrientation = 1 +#If we set useSimulation=0, it sets the arm pose to be the IK result directly without using dynamic control. +#This can be used to test the IK result accuracy. +useSimulation = 1 +useRealTimeSimulation = 0 +ikSolver = 0 +p.setRealTimeSimulation(useRealTimeSimulation) +#trailDuration is duration (in seconds) after debug lines will be removed automatically +#use 0 for no-removal +trailDuration = 15 + +i=0 +while 1: + i+=1 + #p.getCameraImage(320, + # 200, + # flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, + # renderer=p.ER_BULLET_HARDWARE_OPENGL) + if (useRealTimeSimulation): + dt = datetime.now() + t = (dt.second / 60.) * 2. * math.pi + else: + t = t + 0.01 + + if (useSimulation and useRealTimeSimulation == 0): + p.stepSimulation() + + for i in range(1): + pos = [-0.4, 0.2 * math.cos(t), 0. + 0.2 * math.sin(t)] + #end effector points down, not up (in case useOrientation==1) + orn = p.getQuaternionFromEuler([0, -math.pi, 0]) + + if (useNullSpace == 1): + if (useOrientation == 1): + jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, orn, ll, ul, + jr, rp) + else: + jointPoses = p.calculateInverseKinematics(kukaId, + kukaEndEffectorIndex, + pos, + lowerLimits=ll, + upperLimits=ul, + jointRanges=jr, + restPoses=rp) + else: + if (useOrientation == 1): + jointPoses = p.calculateInverseKinematics(kukaId, + kukaEndEffectorIndex, + pos, + orn, + jointDamping=jd, + solver=ikSolver, + maxNumIterations=100, + residualThreshold=.01) + else: + jointPoses = p.calculateInverseKinematics(kukaId, + kukaEndEffectorIndex, + pos, + solver=ikSolver) + + if (useSimulation): + for i in range(numJoints): + p.setJointMotorControl2(bodyIndex=kukaId, + jointIndex=i, + controlMode=p.POSITION_CONTROL, + targetPosition=jointPoses[i], + targetVelocity=0, + force=500, + positionGain=0.03, + velocityGain=1) + else: + #reset the joint state (ignoring all dynamics, not recommended to use during simulation) + for i in range(numJoints): + p.resetJointState(kukaId, i, jointPoses[i]) + + ls = p.getLinkState(kukaId, kukaEndEffectorIndex) + if (hasPrevPose): + p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration) + p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration) + prevPose = pos + prevPose1 = ls[4] + hasPrevPose = 1 +p.disconnect() diff --git a/examples/pybullet/gym/pybullet_examples/inverse_kinematics_husky_kuka.py b/examples/pybullet/gym/pybullet_examples/inverse_kinematics_husky_kuka.py new file mode 100644 index 000000000..bb0e26eaa --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/inverse_kinematics_husky_kuka.py @@ -0,0 +1,202 @@ +import pybullet as p +import time +import math +from datetime import datetime +from datetime import datetime +import pybullet_data + +clid = p.connect(p.SHARED_MEMORY) + + +if (clid < 0): + p.connect(p.GUI) + +p.setPhysicsEngineParameter(enableConeFriction=0) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + + +p.loadURDF("plane.urdf", [0, 0, -0.3]) +husky = p.loadURDF("husky/husky.urdf", [0.290388, 0.329902, -0.310270], + [0.002328, -0.000984, 0.996491, 0.083659]) +for i in range(p.getNumJoints(husky)): + print(p.getJointInfo(husky, i)) +kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf", 0.193749, 0.345564, 0.120208, 0.002327, + -0.000988, 0.996491, 0.083659) +ob = kukaId +jointPositions = [3.559609, 0.411182, 0.862129, 1.744441, 0.077299, -1.129685, 0.006001] +for jointIndex in range(p.getNumJoints(ob)): + p.resetJointState(ob, jointIndex, jointPositions[jointIndex]) + +#put kuka on top of husky + +cid = p.createConstraint(husky, -1, kukaId, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], [0., 0., -.5], + [0, 0, 0, 1]) + +baseorn = p.getQuaternionFromEuler([3.1415, 0, 0.3]) +baseorn = [0, 0, 0, 1] +#[0, 0, 0.707, 0.707] + +#p.resetBasePositionAndOrientation(kukaId,[0,0,0],baseorn)#[0,0,0,1]) +kukaEndEffectorIndex = 6 +numJoints = p.getNumJoints(kukaId) +if (numJoints != 7): + exit() + +#lower limits for null space +ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] +#upper limits for null space +ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] +#joint ranges for null space +jr = [5.8, 4, 5.8, 4, 5.8, 4, 6] +#restposes for null space +rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] +#joint damping coefficents +jd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + +for i in range(numJoints): + p.resetJointState(kukaId, i, rp[i]) + +p.setGravity(0, 0, -10) +t = 0. +prevPose = [0, 0, 0] +prevPose1 = [0, 0, 0] +hasPrevPose = 0 +useNullSpace = 0 + +useOrientation = 0 +#If we set useSimulation=0, it sets the arm pose to be the IK result directly without using dynamic control. +#This can be used to test the IK result accuracy. +useSimulation = 1 +useRealTimeSimulation = 1 +p.setRealTimeSimulation(useRealTimeSimulation) +#trailDuration is duration (in seconds) after debug lines will be removed automatically +#use 0 for no-removal +trailDuration = 15 +basepos = [0, 0, 0] +ang = 0 +ang = 0 + + +def accurateCalculateInverseKinematics(kukaId, endEffectorId, targetPos, threshold, maxIter): + closeEnough = False + iter = 0 + dist2 = 1e30 + while (not closeEnough and iter < maxIter): + jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, targetPos) + for i in range(numJoints): + p.resetJointState(kukaId, i, jointPoses[i]) + ls = p.getLinkState(kukaId, kukaEndEffectorIndex) + newPos = ls[4] + diff = [targetPos[0] - newPos[0], targetPos[1] - newPos[1], targetPos[2] - newPos[2]] + dist2 = (diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2]) + closeEnough = (dist2 < threshold) + iter = iter + 1 + #print ("Num iter: "+str(iter) + "threshold: "+str(dist2)) + return jointPoses + + +wheels = [2, 3, 4, 5] +#(2, b'front_left_wheel', 0, 7, 6, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'front_left_wheel_link') +#(3, b'front_right_wheel', 0, 8, 7, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'front_right_wheel_link') +#(4, b'rear_left_wheel', 0, 9, 8, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'rear_left_wheel_link') +#(5, b'rear_right_wheel', 0, 10, 9, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'rear_right_wheel_link') +wheelVelocities = [0, 0, 0, 0] +wheelDeltasTurn = [1, -1, 1, -1] +wheelDeltasFwd = [1, 1, 1, 1] +while 1: + keys = p.getKeyboardEvents() + shift = 0.01 + wheelVelocities = [0, 0, 0, 0] + speed = 1.0 + for k in keys: + if ord('s') in keys: + p.saveWorld("state.py") + if ord('a') in keys: + basepos = basepos = [basepos[0], basepos[1] - shift, basepos[2]] + if ord('d') in keys: + basepos = basepos = [basepos[0], basepos[1] + shift, basepos[2]] + + if p.B3G_LEFT_ARROW in keys: + for i in range(len(wheels)): + wheelVelocities[i] = wheelVelocities[i] - speed * wheelDeltasTurn[i] + if p.B3G_RIGHT_ARROW in keys: + for i in range(len(wheels)): + wheelVelocities[i] = wheelVelocities[i] + speed * wheelDeltasTurn[i] + if p.B3G_UP_ARROW in keys: + for i in range(len(wheels)): + wheelVelocities[i] = wheelVelocities[i] + speed * wheelDeltasFwd[i] + if p.B3G_DOWN_ARROW in keys: + for i in range(len(wheels)): + wheelVelocities[i] = wheelVelocities[i] - speed * wheelDeltasFwd[i] + + baseorn = p.getQuaternionFromEuler([0, 0, ang]) + for i in range(len(wheels)): + p.setJointMotorControl2(husky, + wheels[i], + p.VELOCITY_CONTROL, + targetVelocity=wheelVelocities[i], + force=1000) + #p.resetBasePositionAndOrientation(kukaId,basepos,baseorn)#[0,0,0,1]) + if (useRealTimeSimulation): + t = time.time() #(dt, micro) = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f').split('.') + #t = (dt.second/60.)*2.*math.pi + else: + t = t + 0.001 + + if (useSimulation and useRealTimeSimulation == 0): + p.stepSimulation() + + for i in range(1): + #pos = [-0.4,0.2*math.cos(t),0.+0.2*math.sin(t)] + pos = [0.2 * math.cos(t), 0, 0. + 0.2 * math.sin(t) + 0.7] + #end effector points down, not up (in case useOrientation==1) + orn = p.getQuaternionFromEuler([0, -math.pi, 0]) + + if (useNullSpace == 1): + if (useOrientation == 1): + jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, orn, ll, ul, + jr, rp) + else: + jointPoses = p.calculateInverseKinematics(kukaId, + kukaEndEffectorIndex, + pos, + lowerLimits=ll, + upperLimits=ul, + jointRanges=jr, + restPoses=rp) + else: + if (useOrientation == 1): + jointPoses = p.calculateInverseKinematics(kukaId, + kukaEndEffectorIndex, + pos, + orn, + jointDamping=jd) + else: + threshold = 0.001 + maxIter = 100 + jointPoses = accurateCalculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, + threshold, maxIter) + + if (useSimulation): + for i in range(numJoints): + p.setJointMotorControl2(bodyIndex=kukaId, + jointIndex=i, + controlMode=p.POSITION_CONTROL, + targetPosition=jointPoses[i], + targetVelocity=0, + force=500, + positionGain=1, + velocityGain=0.1) + else: + #reset the joint state (ignoring all dynamics, not recommended to use during simulation) + for i in range(numJoints): + p.resetJointState(kukaId, i, jointPoses[i]) + + ls = p.getLinkState(kukaId, kukaEndEffectorIndex) + if (hasPrevPose): + p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration) + p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration) + prevPose = pos + prevPose1 = ls[4] + hasPrevPose = 1 diff --git a/examples/pybullet/gym/pybullet_examples/inverse_kinematics_pole.py b/examples/pybullet/gym/pybullet_examples/inverse_kinematics_pole.py new file mode 100644 index 000000000..653581a0e --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/inverse_kinematics_pole.py @@ -0,0 +1,66 @@ +import pybullet as p +import time +import math +from datetime import datetime +import pybullet_data + +clid = p.connect(p.SHARED_MEMORY) +if (clid < 0): + p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.loadURDF("plane.urdf", [0, 0, -1.3]) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +sawyerId = p.loadURDF("pole.urdf", [0, 0, 0]) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) +p.resetBasePositionAndOrientation(sawyerId, [0, 0, 0], [0, 0, 0, 1]) + +sawyerEndEffectorIndex = 3 +numJoints = p.getNumJoints(sawyerId) +#joint damping coefficents +jd = [0.1, 0.1, 0.1, 0.1] + +p.setGravity(0, 0, 0) +t = 0. +prevPose = [0, 0, 0] +prevPose1 = [0, 0, 0] +hasPrevPose = 0 + +ikSolver = 0 +useRealTimeSimulation = 0 +p.setRealTimeSimulation(useRealTimeSimulation) +#trailDuration is duration (in seconds) after debug lines will be removed automatically +#use 0 for no-removal +trailDuration = 1 + +while 1: + if (useRealTimeSimulation): + dt = datetime.now() + t = (dt.second / 60.) * 2. * math.pi + else: + t = t + 0.01 + time.sleep(0.01) + + for i in range(1): + pos = [2. * math.cos(t), 2. * math.cos(t), 0. + 2. * math.sin(t)] + jointPoses = p.calculateInverseKinematics(sawyerId, + sawyerEndEffectorIndex, + pos, + jointDamping=jd, + solver=ikSolver, + maxNumIterations=100) + + #reset the joint state (ignoring all dynamics, not recommended to use during simulation) + for i in range(numJoints): + jointInfo = p.getJointInfo(sawyerId, i) + qIndex = jointInfo[3] + if qIndex > -1: + p.resetJointState(sawyerId, i, jointPoses[qIndex - 7]) + + ls = p.getLinkState(sawyerId, sawyerEndEffectorIndex) + if (hasPrevPose): + p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration) + p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration) + prevPose = pos + prevPose1 = ls[4] + hasPrevPose = 1 diff --git a/examples/pybullet/gym/pybullet_examples/jacobian.py b/examples/pybullet/gym/pybullet_examples/jacobian.py new file mode 100644 index 000000000..4a7e9725c --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/jacobian.py @@ -0,0 +1,102 @@ +import pybullet as p +import pybullet_data + + + +def getJointStates(robot): + joint_states = p.getJointStates(robot, range(p.getNumJoints(robot))) + joint_positions = [state[0] for state in joint_states] + joint_velocities = [state[1] for state in joint_states] + joint_torques = [state[3] for state in joint_states] + return joint_positions, joint_velocities, joint_torques + + +def getMotorJointStates(robot): + joint_states = p.getJointStates(robot, range(p.getNumJoints(robot))) + joint_infos = [p.getJointInfo(robot, i) for i in range(p.getNumJoints(robot))] + joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1] + joint_positions = [state[0] for state in joint_states] + joint_velocities = [state[1] for state in joint_states] + joint_torques = [state[3] for state in joint_states] + return joint_positions, joint_velocities, joint_torques + + +def setJointPosition(robot, position, kp=1.0, kv=0.3): + num_joints = p.getNumJoints(robot) + zero_vec = [0.0] * num_joints + if len(position) == num_joints: + p.setJointMotorControlArray(robot, + range(num_joints), + p.POSITION_CONTROL, + targetPositions=position, + targetVelocities=zero_vec, + positionGains=[kp] * num_joints, + velocityGains=[kv] * num_joints) + else: + print("Not setting torque. " + "Expected torque vector of " + "length {}, got {}".format(num_joints, len(torque))) + + +def multiplyJacobian(robot, jacobian, vector): + result = [0.0, 0.0, 0.0] + i = 0 + for c in range(len(vector)): + if p.getJointInfo(robot, c)[3] > -1: + for r in range(3): + result[r] += jacobian[r][i] * vector[c] + i += 1 + return result + + +clid = p.connect(p.SHARED_MEMORY) +if (clid < 0): + p.connect(p.DIRECT) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +time_step = 0.001 +gravity_constant = -9.81 +p.resetSimulation() +p.setTimeStep(time_step) +p.setGravity(0.0, 0.0, gravity_constant) + +p.loadURDF("plane.urdf", [0, 0, -0.3]) + +kukaId = p.loadURDF("TwoJointRobot_w_fixedJoints.urdf", useFixedBase=True) +#kukaId = p.loadURDF("TwoJointRobot_w_fixedJoints.urdf",[0,0,0]) +#kukaId = p.loadURDF("kuka_iiwa/model.urdf",[0,0,0]) +#kukaId = p.loadURDF("kuka_lwr/kuka.urdf",[0,0,0]) +#kukaId = p.loadURDF("humanoid/nao.urdf",[0,0,0]) +p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1]) +numJoints = p.getNumJoints(kukaId) +kukaEndEffectorIndex = numJoints - 1 + +# Set a joint target for the position control and step the sim. +setJointPosition(kukaId, [0.1] * numJoints) +p.stepSimulation() + +# Get the joint and link state directly from Bullet. +pos, vel, torq = getJointStates(kukaId) +mpos, mvel, mtorq = getMotorJointStates(kukaId) + +result = p.getLinkState(kukaId, + kukaEndEffectorIndex, + computeLinkVelocity=1, + computeForwardKinematics=1) +link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = result +# Get the Jacobians for the CoM of the end-effector link. +# Note that in this example com_rot = identity, and we would need to use com_rot.T * com_trn. +# The localPosition is always defined in terms of the link frame coordinates. + +zero_vec = [0.0] * len(mpos) +jac_t, jac_r = p.calculateJacobian(kukaId, kukaEndEffectorIndex, com_trn, mpos, zero_vec, zero_vec) + +print("Link linear velocity of CoM from getLinkState:") +print(link_vt) +print("Link linear velocity of CoM from linearJacobian * q_dot:") +print(multiplyJacobian(kukaId, jac_t, vel)) +print("Link angular velocity of CoM from getLinkState:") +print(link_vr) +print("Link angular velocity of CoM from angularJacobian * q_dot:") +print(multiplyJacobian(kukaId, jac_r, vel)) diff --git a/examples/pybullet/gym/pybullet_examples/manyspheres.py b/examples/pybullet/gym/pybullet_examples/manyspheres.py new file mode 100644 index 000000000..4c6e5197c --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/manyspheres.py @@ -0,0 +1,38 @@ +import pybullet as p +import time +import pybullet_data + + +conid = p.connect(p.SHARED_MEMORY) +if (conid < 0): + p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setInternalSimFlags(0) +p.resetSimulation() + +p.loadURDF("plane.urdf", useMaximalCoordinates=True) +p.loadURDF("tray/traybox.urdf", useMaximalCoordinates=True) + +gravXid = p.addUserDebugParameter("gravityX", -10, 10, 0) +gravYid = p.addUserDebugParameter("gravityY", -10, 10, 0) +gravZid = p.addUserDebugParameter("gravityZ", -10, 10, -10) +p.setPhysicsEngineParameter(numSolverIterations=10) +p.setPhysicsEngineParameter(contactBreakingThreshold=0.001) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +for i in range(10): + for j in range(10): + for k in range(10): + ob = p.loadURDF("sphere_1cm.urdf", [0.02 * i, 0.02 * j, 0.2 + 0.02 * k], + useMaximalCoordinates=True) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) +p.setGravity(0, 0, -10) + +p.setRealTimeSimulation(1) +while True: + gravX = p.readUserDebugParameter(gravXid) + gravY = p.readUserDebugParameter(gravYid) + gravZ = p.readUserDebugParameter(gravZid) + p.setGravity(gravX, gravY, gravZ) + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/mimicJointConstraint.py b/examples/pybullet/gym/pybullet_examples/mimicJointConstraint.py new file mode 100644 index 000000000..7a54f5c10 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/mimicJointConstraint.py @@ -0,0 +1,50 @@ +#a mimic joint can act as a gear between two joints +#you can control the gear ratio in magnitude and sign (>0 reverses direction) + +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.loadURDF("plane.urdf", 0, 0, -2) +wheelA = p.loadURDF("differential/diff_ring.urdf", [0, 0, 0]) +for i in range(p.getNumJoints(wheelA)): + print(p.getJointInfo(wheelA, i)) + p.setJointMotorControl2(wheelA, i, p.VELOCITY_CONTROL, targetVelocity=0, force=0) + +c = p.createConstraint(wheelA, + 1, + wheelA, + 3, + jointType=p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) +p.changeConstraint(c, gearRatio=1, maxForce=10000) + +c = p.createConstraint(wheelA, + 2, + wheelA, + 4, + jointType=p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) +p.changeConstraint(c, gearRatio=-1, maxForce=10000) + +c = p.createConstraint(wheelA, + 1, + wheelA, + 4, + jointType=p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) +p.changeConstraint(c, gearRatio=-1, maxForce=10000) + +p.setRealTimeSimulation(1) +while (1): + p.setGravity(0, 0, -10) + time.sleep(0.01) +#p.removeConstraint(c) diff --git a/examples/pybullet/gym/pybullet_examples/motorMaxVelocity.py b/examples/pybullet/gym/pybullet_examples/motorMaxVelocity.py new file mode 100644 index 000000000..95a0c1e39 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/motorMaxVelocity.py @@ -0,0 +1,22 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +cartpole = p.loadURDF("cartpole.urdf") +p.setRealTimeSimulation(1) +p.setJointMotorControl2(cartpole, + 1, + p.POSITION_CONTROL, + targetPosition=1000, + targetVelocity=0, + force=1000, + positionGain=1, + velocityGain=0, + maxVelocity=0.5) +while (1): + p.setGravity(0, 0, -10) + js = p.getJointState(cartpole, 1) + print("position=", js[0], "velocity=", js[1]) + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/pdControl.py b/examples/pybullet/gym/pybullet_examples/pdControl.py new file mode 100644 index 000000000..e9c902e23 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/pdControl.py @@ -0,0 +1,152 @@ +import pybullet as p +from pdControllerExplicit import PDControllerExplicitMultiDof +from pdControllerExplicit import PDControllerExplicit +from pdControllerStable import PDControllerStable + +import time + +useMaximalCoordinates = False +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +pole = p.loadURDF("cartpole.urdf", [0, 0, 0], useMaximalCoordinates=useMaximalCoordinates) +pole2 = p.loadURDF("cartpole.urdf", [0, 1, 0], useMaximalCoordinates=useMaximalCoordinates) +pole3 = p.loadURDF("cartpole.urdf", [0, 2, 0], useMaximalCoordinates=useMaximalCoordinates) +pole4 = p.loadURDF("cartpole.urdf", [0, 3, 0], useMaximalCoordinates=useMaximalCoordinates) + +exPD = PDControllerExplicitMultiDof(p) +sPD = PDControllerStable(p) + +for i in range(p.getNumJoints(pole2)): + #disable default constraint-based motors + p.setJointMotorControl2(pole, i, p.POSITION_CONTROL, targetPosition=0, force=0) + p.setJointMotorControl2(pole2, i, p.POSITION_CONTROL, targetPosition=0, force=0) + p.setJointMotorControl2(pole3, i, p.POSITION_CONTROL, targetPosition=0, force=0) + p.setJointMotorControl2(pole4, i, p.POSITION_CONTROL, targetPosition=0, force=0) + + #print("joint",i,"=",p.getJointInfo(pole2,i)) + +timeStepId = p.addUserDebugParameter("timeStep", 0.001, 0.1, 0.01) +desiredPosCartId = p.addUserDebugParameter("desiredPosCart", -10, 10, 2) +desiredVelCartId = p.addUserDebugParameter("desiredVelCart", -10, 10, 0) +kpCartId = p.addUserDebugParameter("kpCart", 0, 500, 1300) +kdCartId = p.addUserDebugParameter("kdCart", 0, 300, 150) +maxForceCartId = p.addUserDebugParameter("maxForceCart", 0, 5000, 1000) + +textColor = [1, 1, 1] +shift = 0.05 +p.addUserDebugText("explicit PD", [shift, 0, .1], + textColor, + parentObjectUniqueId=pole, + parentLinkIndex=1) +p.addUserDebugText("explicit PD plugin", [shift, 0, -.1], + textColor, + parentObjectUniqueId=pole2, + parentLinkIndex=1) +p.addUserDebugText("stablePD", [shift, 0, .1], + textColor, + parentObjectUniqueId=pole4, + parentLinkIndex=1) +p.addUserDebugText("position constraint", [shift, 0, -.1], + textColor, + parentObjectUniqueId=pole3, + parentLinkIndex=1) + +desiredPosPoleId = p.addUserDebugParameter("desiredPosPole", -10, 10, 0) +desiredVelPoleId = p.addUserDebugParameter("desiredVelPole", -10, 10, 0) +kpPoleId = p.addUserDebugParameter("kpPole", 0, 500, 1200) +kdPoleId = p.addUserDebugParameter("kdPole", 0, 300, 100) +maxForcePoleId = p.addUserDebugParameter("maxForcePole", 0, 5000, 1000) + +pd = p.loadPlugin("pdControlPlugin") + +p.setGravity(0, 0, -10) + +useRealTimeSim = False + +p.setRealTimeSimulation(useRealTimeSim) + +timeStep = 0.001 + +while p.isConnected(): + #p.getCameraImage(320,200) + timeStep = p.readUserDebugParameter(timeStepId) + p.setTimeStep(timeStep) + + desiredPosCart = p.readUserDebugParameter(desiredPosCartId) + desiredVelCart = p.readUserDebugParameter(desiredVelCartId) + kpCart = p.readUserDebugParameter(kpCartId) + kdCart = p.readUserDebugParameter(kdCartId) + maxForceCart = p.readUserDebugParameter(maxForceCartId) + + desiredPosPole = p.readUserDebugParameter(desiredPosPoleId) + desiredVelPole = p.readUserDebugParameter(desiredVelPoleId) + kpPole = p.readUserDebugParameter(kpPoleId) + kdPole = p.readUserDebugParameter(kdPoleId) + maxForcePole = p.readUserDebugParameter(maxForcePoleId) + + basePos, baseOrn = p.getBasePositionAndOrientation(pole) + + baseDof = 7 + taus = exPD.computePD(pole, [0, 1], [ + basePos[0], basePos[1], basePos[2], baseOrn[0], baseOrn[1], baseOrn[2], baseOrn[3], + desiredPosCart, desiredPosPole + ], [0, 0, 0, 0, 0, 0, 0, desiredVelCart, desiredVelPole], [0, 0, 0, 0, 0, 0, 0, kpCart, kpPole], + [0, 0, 0, 0, 0, 0, 0, kdCart, kdPole], + [0, 0, 0, 0, 0, 0, 0, maxForceCart, maxForcePole], timeStep) + + for j in [0, 1]: + p.setJointMotorControlMultiDof(pole, + j, + controlMode=p.TORQUE_CONTROL, + force=[taus[j + baseDof]]) + #p.setJointMotorControlArray(pole, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus) + + if (pd >= 0): + link = 0 + p.setJointMotorControl2(bodyUniqueId=pole2, + jointIndex=link, + controlMode=p.PD_CONTROL, + targetPosition=desiredPosCart, + targetVelocity=desiredVelCart, + force=maxForceCart, + positionGain=kpCart, + velocityGain=kdCart) + link = 1 + p.setJointMotorControl2(bodyUniqueId=pole2, + jointIndex=link, + controlMode=p.PD_CONTROL, + targetPosition=desiredPosPole, + targetVelocity=desiredVelPole, + force=maxForcePole, + positionGain=kpPole, + velocityGain=kdPole) + + taus = sPD.computePD(pole4, [0, 1], [desiredPosCart, desiredPosPole], + [desiredVelCart, desiredVelPole], [kpCart, kpPole], [kdCart, kdPole], + [maxForceCart, maxForcePole], timeStep) + #p.setJointMotorControlArray(pole4, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus) + for j in [0, 1]: + p.setJointMotorControlMultiDof(pole4, j, controlMode=p.TORQUE_CONTROL, force=[taus[j]]) + + p.setJointMotorControl2(pole3, + 0, + p.POSITION_CONTROL, + targetPosition=desiredPosCart, + targetVelocity=desiredVelCart, + positionGain=timeStep * (kpCart / 150.), + velocityGain=0.5, + force=maxForceCart) + p.setJointMotorControl2(pole3, + 1, + p.POSITION_CONTROL, + targetPosition=desiredPosPole, + targetVelocity=desiredVelPole, + positionGain=timeStep * (kpPole / 150.), + velocityGain=0.5, + force=maxForcePole) + + if (not useRealTimeSim): + p.stepSimulation() + time.sleep(timeStep) diff --git a/examples/pybullet/gym/pybullet_examples/pdControllerExplicit.py b/examples/pybullet/gym/pybullet_examples/pdControllerExplicit.py new file mode 100644 index 000000000..20cd0a65e --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/pdControllerExplicit.py @@ -0,0 +1,98 @@ +import numpy as np + + +class PDControllerExplicitMultiDof(object): + + def __init__(self, pb): + self._pb = pb + + def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, + maxForces, timeStep): + + numJoints = len(jointIndices) #self._pb.getNumJoints(bodyUniqueId) + curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId) + q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]] + baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId) + qdot1 = [ + baseLinVel[0], baseLinVel[1], baseLinVel[2], baseAngVel[0], baseAngVel[1], baseAngVel[2], 0 + ] + qError = [0, 0, 0, 0, 0, 0, 0] + qIndex = 7 + qdotIndex = 7 + zeroAccelerations = [0, 0, 0, 0, 0, 0, 0] + for i in range(numJoints): + js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i]) + + jointPos = js[0] + jointVel = js[1] + q1 += jointPos + + if len(js[0]) == 1: + desiredPos = desiredPositions[qIndex] + + qdiff = desiredPos - jointPos[0] + qError.append(qdiff) + zeroAccelerations.append(0.) + qdot1 += jointVel + qIndex += 1 + qdotIndex += 1 + if len(js[0]) == 4: + desiredPos = [ + desiredPositions[qIndex], desiredPositions[qIndex + 1], desiredPositions[qIndex + 2], + desiredPositions[qIndex + 3] + ] + axis = self._pb.getAxisDifferenceQuaternion(desiredPos, jointPos) + jointVelNew = [jointVel[0], jointVel[1], jointVel[2], 0] + qdot1 += jointVelNew + qError.append(axis[0]) + qError.append(axis[1]) + qError.append(axis[2]) + qError.append(0) + desiredVel = [ + desiredVelocities[qdotIndex], desiredVelocities[qdotIndex + 1], + desiredVelocities[qdotIndex + 2] + ] + zeroAccelerations += [0., 0., 0., 0.] + qIndex += 4 + qdotIndex += 4 + + q = np.array(q1) + qdot = np.array(qdot1) + qdotdesired = np.array(desiredVelocities) + qdoterr = qdotdesired - qdot + Kp = np.diagflat(kps) + Kd = np.diagflat(kds) + p = Kp.dot(qError) + d = Kd.dot(qdoterr) + forces = p + d + maxF = np.array(maxForces) + forces = np.clip(forces, -maxF, maxF) + return forces + + +class PDControllerExplicit(object): + + def __init__(self, pb): + self._pb = pb + + def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, + maxForces, timeStep): + numJoints = self._pb.getNumJoints(bodyUniqueId) + jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices) + q1 = [] + qdot1 = [] + for i in range(numJoints): + q1.append(jointStates[i][0]) + qdot1.append(jointStates[i][1]) + q = np.array(q1) + qdot = np.array(qdot1) + qdes = np.array(desiredPositions) + qdotdes = np.array(desiredVelocities) + qError = qdes - q + qdotError = qdotdes - qdot + Kp = np.diagflat(kps) + Kd = np.diagflat(kds) + forces = Kp.dot(qError) + Kd.dot(qdotError) + maxF = np.array(maxForces) + forces = np.clip(forces, -maxF, maxF) + return forces diff --git a/examples/pybullet/gym/pybullet_examples/pdControllerStable.py b/examples/pybullet/gym/pybullet_examples/pdControllerStable.py new file mode 100644 index 000000000..bec3356c4 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/pdControllerStable.py @@ -0,0 +1,147 @@ +import numpy as np + + +class PDControllerStableMultiDof(object): + + def __init__(self, pb): + self._pb = pb + + def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, + maxForces, timeStep): + + numJoints = len(jointIndices) #self._pb.getNumJoints(bodyUniqueId) + curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId) + #q1 = [desiredPositions[0],desiredPositions[1],desiredPositions[2],desiredPositions[3],desiredPositions[4],desiredPositions[5],desiredPositions[6]] + q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]] + + #qdot1 = [0,0,0, 0,0,0,0] + baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId) + + qdot1 = [ + baseLinVel[0], baseLinVel[1], baseLinVel[2], baseAngVel[0], baseAngVel[1], baseAngVel[2], 0 + ] + qError = [0, 0, 0, 0, 0, 0, 0] + + qIndex = 7 + qdotIndex = 7 + zeroAccelerations = [0, 0, 0, 0, 0, 0, 0] + for i in range(numJoints): + js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i]) + + jointPos = js[0] + jointVel = js[1] + q1 += jointPos + + if len(js[0]) == 1: + desiredPos = desiredPositions[qIndex] + + qdiff = desiredPos - jointPos[0] + qError.append(qdiff) + zeroAccelerations.append(0.) + qdot1 += jointVel + qIndex += 1 + qdotIndex += 1 + if len(js[0]) == 4: + desiredPos = [ + desiredPositions[qIndex], desiredPositions[qIndex + 1], desiredPositions[qIndex + 2], + desiredPositions[qIndex + 3] + ] + axis = self._pb.getAxisDifferenceQuaternion(desiredPos, jointPos) + jointVelNew = [jointVel[0], jointVel[1], jointVel[2], 0] + qdot1 += jointVelNew + qError.append(axis[0]) + qError.append(axis[1]) + qError.append(axis[2]) + qError.append(0) + desiredVel = [ + desiredVelocities[qdotIndex], desiredVelocities[qdotIndex + 1], + desiredVelocities[qdotIndex + 2] + ] + zeroAccelerations += [0., 0., 0., 0.] + qIndex += 4 + qdotIndex += 4 + + q = np.array(q1) + qdot = np.array(qdot1) + + qdotdesired = np.array(desiredVelocities) + qdoterr = qdotdesired - qdot + + Kp = np.diagflat(kps) + Kd = np.diagflat(kds) + + # Compute -Kp(q + qdot - qdes) + p_term = Kp.dot(qError - qdot*timeStep) + # Compute -Kd(qdot - qdotdes) + d_term = Kd.dot(qdoterr) + + # Compute Inertia matrix M(q) + M = self._pb.calculateMassMatrix(bodyUniqueId, q1, flags=1) + M = np.array(M) + # Given: M(q) * qddot + C(q, qdot) = T_ext + T_int + # Compute Coriolis and External (Gravitational) terms G = C - T_ext + G = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1) + G = np.array(G) + # Obtain estimated generalized accelerations, considering Coriolis and Gravitational forces, and stable PD actions + qddot = np.linalg.solve(a=(M + Kd * timeStep), + b=p_term + d_term - G) + # Compute control generalized forces (T_int) + tau = p_term + d_term - Kd.dot(qddot) * timeStep + # Clip generalized forces to actuator limits + maxF = np.array(maxForces) + generalized_forces = np.clip(tau, -maxF, maxF) + return generalized_forces + + +class PDControllerStable(object): + """ + Implementation based on: Tan, J., Liu, K., & Turk, G. (2011). "Stable proportional-derivative controllers" + DOI: 10.1109/MCG.2011.30 + """ + def __init__(self, pb): + self._pb = pb + + def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, + maxForces, timeStep): + numJoints = self._pb.getNumJoints(bodyUniqueId) + jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices) + q1 = [] + qdot1 = [] + zeroAccelerations = [] + for i in range(numJoints): + q1.append(jointStates[i][0]) + qdot1.append(jointStates[i][1]) + zeroAccelerations.append(0) + + q = np.array(q1) + qdot = np.array(qdot1) + qdes = np.array(desiredPositions) + qdotdes = np.array(desiredVelocities) + + qError = qdes - q + qdotError = qdotdes - qdot + + Kp = np.diagflat(kps) + Kd = np.diagflat(kds) + + # Compute -Kp(q + qdot - qdes) + p_term = Kp.dot(qError - qdot*timeStep) + # Compute -Kd(qdot - qdotdes) + d_term = Kd.dot(qdotError) + + # Compute Inertia matrix M(q) + M = self._pb.calculateMassMatrix(bodyUniqueId, q1) + M = np.array(M) + # Given: M(q) * qddot + C(q, qdot) = T_ext + T_int + # Compute Coriolis and External (Gravitational) terms G = C - T_ext + G = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations) + G = np.array(G) + # Obtain estimated generalized accelerations, considering Coriolis and Gravitational forces, and stable PD actions + qddot = np.linalg.solve(a=(M + Kd * timeStep), + b=(-G + p_term + d_term)) + # Compute control generalized forces (T_int) + tau = p_term + d_term - (Kd.dot(qddot) * timeStep) + # Clip generalized forces to actuator limits + maxF = np.array(maxForces) + generalized_forces = np.clip(tau, -maxF, maxF) + return generalized_forces diff --git a/examples/pybullet/gym/pybullet_examples/pointCloudFromCameraImage.py b/examples/pybullet/gym/pybullet_examples/pointCloudFromCameraImage.py new file mode 100644 index 000000000..65d88149c --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/pointCloudFromCameraImage.py @@ -0,0 +1,138 @@ +import pybullet as p +import math +import numpy as np +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +plane = p.loadURDF("plane100.urdf") +cube = p.loadURDF("cube.urdf", [0, 0, 1]) + + +def getRayFromTo(mouseX, mouseY): + width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera( + ) + camPos = [ + camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1], + camTarget[2] - dist * camForward[2] + ] + farPlane = 10000 + rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])] + lenFwd = math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] * rayForward[1] + + rayForward[2] * rayForward[2]) + invLen = farPlane * 1. / lenFwd + rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]] + rayFrom = camPos + oneOverWidth = float(1) / float(width) + oneOverHeight = float(1) / float(height) + + dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth] + dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight] + rayToCenter = [ + rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2] + ] + ortho = [ + -0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] - float(mouseY) * dVer[0], + -0.5 * horizon[1] + 0.5 * vertical[1] + float(mouseX) * dHor[1] - float(mouseY) * dVer[1], + -0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2] + ] + + rayTo = [ + rayFrom[0] + rayForward[0] + ortho[0], rayFrom[1] + rayForward[1] + ortho[1], + rayFrom[2] + rayForward[2] + ortho[2] + ] + lenOrtho = math.sqrt(ortho[0] * ortho[0] + ortho[1] * ortho[1] + ortho[2] * ortho[2]) + alpha = math.atan(lenOrtho / farPlane) + return rayFrom, rayTo, alpha + + +width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera( +) +camPos = [ + camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1], + camTarget[2] - dist * camForward[2] +] +farPlane = 10000 +rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])] +lenFwd = math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] * rayForward[1] + + rayForward[2] * rayForward[2]) +oneOverWidth = float(1) / float(width) +oneOverHeight = float(1) / float(height) +dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth] +dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight] + +lendHor = math.sqrt(dHor[0] * dHor[0] + dHor[1] * dHor[1] + dHor[2] * dHor[2]) +lendVer = math.sqrt(dVer[0] * dVer[0] + dVer[1] * dVer[1] + dVer[2] * dVer[2]) + +cornersX = [0, width, width, 0] +cornersY = [0, 0, height, height] +corners3D = [] + +imgW = int(width / 4) +imgH = int(height / 4) + +img = p.getCameraImage(imgW, imgH, renderer=p.ER_BULLET_HARDWARE_OPENGL) +rgbBuffer = img[2] +depthBuffer = img[3] +print("rgbBuffer.shape=", rgbBuffer.shape) +print("depthBuffer.shape=", depthBuffer.shape) + +#disable rendering temporary makes adding objects faster +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) +p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0) +visualShapeId = p.createVisualShape(shapeType=p.GEOM_SPHERE, rgbaColor=[1, 1, 1, 1], radius=0.03) +collisionShapeId = -1 #p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="duck_vhacd.obj", collisionFramePosition=shift,meshScale=meshScale) + +for i in range(4): + w = cornersX[i] + h = cornersY[i] + rayFrom, rayTo, _ = getRayFromTo(w, h) + rf = np.array(rayFrom) + rt = np.array(rayTo) + vec = rt - rf + l = np.sqrt(np.dot(vec, vec)) + newTo = (0.01 / l) * vec + rf + #print("len vec=",np.sqrt(np.dot(vec,vec))) + + p.addUserDebugLine(rayFrom, newTo, [1, 0, 0]) + corners3D.append(newTo) +count = 0 + +stepX = 5 +stepY = 5 +for w in range(0, imgW, stepX): + for h in range(0, imgH, stepY): + count += 1 + if ((count % 100) == 0): + print(count, "out of ", imgW * imgH / (stepX * stepY)) + rayFrom, rayTo, alpha = getRayFromTo(w * (width / imgW), h * (height / imgH)) + rf = np.array(rayFrom) + rt = np.array(rayTo) + vec = rt - rf + l = np.sqrt(np.dot(vec, vec)) + depthImg = float(depthBuffer[h, w]) + far = 1000. + near = 0.01 + depth = far * near / (far - (far - near) * depthImg) + depth /= math.cos(alpha) + newTo = (depth / l) * vec + rf + #p.addUserDebugLine(rayFrom, newTo, [1, 0, 0]) + mb = p.createMultiBody(baseMass=0, + baseCollisionShapeIndex=collisionShapeId, + baseVisualShapeIndex=visualShapeId, + basePosition=newTo, + useMaximalCoordinates=True) + color = rgbBuffer[h, w] + color = [color[0] / 255., color[1] / 255., color[2] / 255., 1] + p.changeVisualShape(mb, -1, rgbaColor=color) +p.addUserDebugLine(corners3D[0], corners3D[1], [1, 0, 0]) +p.addUserDebugLine(corners3D[1], corners3D[2], [1, 0, 0]) +p.addUserDebugLine(corners3D[2], corners3D[3], [1, 0, 0]) +p.addUserDebugLine(corners3D[3], corners3D[0], [1, 0, 0]) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) +print("ready\n") +#p.removeBody(plane) +#p.removeBody(cube) +while (1): + p.setGravity(0, 0, -10) diff --git a/examples/pybullet/gym/pybullet_examples/profileTiming.py b/examples/pybullet/gym/pybullet_examples/profileTiming.py new file mode 100644 index 000000000..96c25b300 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/profileTiming.py @@ -0,0 +1,25 @@ +import pybullet as p +import time +#you can visualize the timings using Google Chrome, visit about://tracing +#and load the json file +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +t = time.time() + 3.1 + +logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "chrome_about_tracing.json") +while (time.time() < t): + p.stepSimulation() + p.submitProfileTiming("pythontest") + time.sleep(1./240.) + p.submitProfileTiming("nested") + for i in range (100): + p.submitProfileTiming("deep_nested") + p.submitProfileTiming() + time.sleep(1./1000.) + p.submitProfileTiming() + p.submitProfileTiming() + +p.stopStateLogging(logId) diff --git a/examples/pybullet/gym/pybullet_examples/projective_texture.py b/examples/pybullet/gym/pybullet_examples/projective_texture.py new file mode 100644 index 000000000..6116c59b0 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/projective_texture.py @@ -0,0 +1,41 @@ +import pybullet as p +from time import sleep +import matplotlib.pyplot as plt +import numpy as np +import pybullet_data + +physicsClient = p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setGravity(0, 0, 0) +bearStartPos1 = [-3.3, 0, 0] +bearStartOrientation1 = p.getQuaternionFromEuler([0, 0, 0]) +bearId1 = p.loadURDF("plane.urdf", bearStartPos1, bearStartOrientation1) +bearStartPos2 = [0, 0, 0] +bearStartOrientation2 = p.getQuaternionFromEuler([0, 0, 0]) +bearId2 = p.loadURDF("teddy_large.urdf", bearStartPos2, bearStartOrientation2) +textureId = p.loadTexture("checker_grid.jpg") +#p.changeVisualShape(objectUniqueId=0, linkIndex=-1, textureUniqueId=textureId) +#p.changeVisualShape(objectUniqueId=1, linkIndex=-1, textureUniqueId=textureId) + +useRealTimeSimulation = 1 + +if (useRealTimeSimulation): + p.setRealTimeSimulation(1) + +while 1: + if (useRealTimeSimulation): + camera = p.getDebugVisualizerCamera() + viewMat = camera[2] + projMat = camera[3] + #An example of setting the view matrix for the projective texture. + #viewMat = p.computeViewMatrix(cameraEyePosition=[7,0,0], cameraTargetPosition=[0,0,0], cameraUpVector=[0,0,1]) + p.getCameraImage(300, + 300, + renderer=p.ER_BULLET_HARDWARE_OPENGL, + flags=p.ER_USE_PROJECTIVE_TEXTURE, + projectiveTextureView=viewMat, + projectiveTextureProj=projMat) + p.setGravity(0, 0, 0) + else: + p.stepSimulation() diff --git a/examples/pybullet/gym/pybullet_examples/rollPitchYaw.py b/examples/pybullet/gym/pybullet_examples/rollPitchYaw.py new file mode 100644 index 000000000..e5dac10ba --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/rollPitchYaw.py @@ -0,0 +1,28 @@ +import pybullet as p +import time +import pybullet_data + +cid = p.connect(p.SHARED_MEMORY) +if (cid < 0): + p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +q = p.loadURDF("quadruped/quadruped.urdf", useFixedBase=True) +rollId = p.addUserDebugParameter("roll", -1.5, 1.5, 0) +pitchId = p.addUserDebugParameter("pitch", -1.5, 1.5, 0) +yawId = p.addUserDebugParameter("yaw", -1.5, 1.5, 0) +fwdxId = p.addUserDebugParameter("fwd_x", -1, 1, 0) +fwdyId = p.addUserDebugParameter("fwd_y", -1, 1, 0) +fwdzId = p.addUserDebugParameter("fwd_z", -1, 1, 0) + +while True: + roll = p.readUserDebugParameter(rollId) + pitch = p.readUserDebugParameter(pitchId) + yaw = p.readUserDebugParameter(yawId) + x = p.readUserDebugParameter(fwdxId) + y = p.readUserDebugParameter(fwdyId) + z = p.readUserDebugParameter(fwdzId) + + orn = p.getQuaternionFromEuler([roll, pitch, yaw]) + p.resetBasePositionAndOrientation(q, [x, y, z], orn) + #p.stepSimulation()#not really necessary for this demo, no physics used diff --git a/examples/pybullet/gym/pybullet_examples/satCollision.py b/examples/pybullet/gym/pybullet_examples/satCollision.py new file mode 100644 index 000000000..af68e143f --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/satCollision.py @@ -0,0 +1,21 @@ +import pybullet as p +import time + +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.setGravity(0, 0, -10) +p.setPhysicsEngineParameter(enableSAT=1) +p.loadURDF("cube_concave.urdf", [0, 0, -25], + globalScaling=50, + useFixedBase=True, + flags=p.URDF_INITIALIZE_SAT_FEATURES) +p.loadURDF("cube.urdf", [0, 0, 1], globalScaling=1, flags=p.URDF_INITIALIZE_SAT_FEATURES) +p.loadURDF("duck_vhacd.urdf", [1, 0, 1], globalScaling=1, flags=p.URDF_INITIALIZE_SAT_FEATURES) + +while (p.isConnected()): + p.stepSimulation() + pts = p.getContactPoints() + #print("num contacts = ", len(pts)) + time.sleep(1. / 240.) diff --git a/examples/pybullet/gym/pybullet_examples/sceneAabb.py b/examples/pybullet/gym/pybullet_examples/sceneAabb.py new file mode 100644 index 000000000..260fa4f64 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/sceneAabb.py @@ -0,0 +1,42 @@ +import pybullet as p +import time +import numpy as np + +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.loadURDF("plane.urdf") +p.loadURDF("sphere2.urdf",[0,0,2]) + +dt = 1./240. +p.setTimeStep(dt) + +def getSceneAABB(): + aabbMins=[] + aabbMaxs=[] + + for i in range(p.getNumBodies()): + uid = p.getBodyUniqueId(i) + aabb = p.getAABB(uid) + aabbMins.append(np.array(aabb[0])) + aabbMaxs.append(np.array(aabb[1])) + + if len(aabbMins): + sceneAABBMin = aabbMins[0] + sceneAABBMax = aabbMaxs[0] + + for aabb in aabbMins: + sceneAABBMin = np.minimum(sceneAABBMin,aabb) + for aabb in aabbMaxs: + sceneAABBMax = np.maximum(sceneAABBMax,aabb) + + print("sceneAABBMin=",sceneAABBMin) + print("sceneAABBMax=",sceneAABBMax) + +getSceneAABB() + +while (1): + p.stepSimulation() + time.sleep(dt) + diff --git a/examples/pybullet/gym/pybullet_examples/signedDistanceField.py b/examples/pybullet/gym/pybullet_examples/signedDistanceField.py new file mode 100644 index 000000000..e1f94fc00 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/signedDistanceField.py @@ -0,0 +1,17 @@ +import pybullet as p +import pybullet +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.loadURDF("toys/concave_box.urdf") +p.setGravity(0, 0, -10) +for i in range(10): + p.loadURDF("sphere_1cm.urdf", [i * 0.02, 0, 0.5]) +p.loadURDF("duck_vhacd.urdf") +timeStep = 1. / 240. +p.setTimeStep(timeStep) +while (1): + p.stepSimulation() + time.sleep(timeStep) diff --git a/examples/pybullet/gym/pybullet_examples/snake.py b/examples/pybullet/gym/pybullet_examples/snake.py new file mode 100644 index 000000000..d70a50e0a --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/snake.py @@ -0,0 +1,144 @@ +import pybullet as p +import time +import math + +# This simple snake logic is from some 15 year old Havok C++ demo +# Thanks to Michael Ewert! +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +plane = p.createCollisionShape(p.GEOM_PLANE) + +p.createMultiBody(0, plane) + +useMaximalCoordinates = True +sphereRadius = 0.25 +#colBoxId = p.createCollisionShapeArray([p.GEOM_BOX, p.GEOM_SPHERE],radii=[sphereRadius+0.03,sphereRadius+0.03], halfExtents=[[sphereRadius,sphereRadius,sphereRadius],[sphereRadius,sphereRadius,sphereRadius]]) +colBoxId = p.createCollisionShape(p.GEOM_BOX, + halfExtents=[sphereRadius, sphereRadius, sphereRadius]) + +mass = 1 +visualShapeId = -1 + +link_Masses = [] +linkCollisionShapeIndices = [] +linkVisualShapeIndices = [] +linkPositions = [] +linkOrientations = [] +linkInertialFramePositions = [] +linkInertialFrameOrientations = [] +indices = [] +jointTypes = [] +axis = [] + +for i in range(36): + link_Masses.append(1) + linkCollisionShapeIndices.append(colBoxId) + linkVisualShapeIndices.append(-1) + linkPositions.append([0, sphereRadius * 2.0 + 0.01, 0]) + linkOrientations.append([0, 0, 0, 1]) + linkInertialFramePositions.append([0, 0, 0]) + linkInertialFrameOrientations.append([0, 0, 0, 1]) + indices.append(i) + jointTypes.append(p.JOINT_REVOLUTE) + axis.append([0, 0, 1]) + +basePosition = [0, 0, 1] +baseOrientation = [0, 0, 0, 1] +sphereUid = p.createMultiBody(mass, + colBoxId, + visualShapeId, + basePosition, + baseOrientation, + linkMasses=link_Masses, + linkCollisionShapeIndices=linkCollisionShapeIndices, + linkVisualShapeIndices=linkVisualShapeIndices, + linkPositions=linkPositions, + linkOrientations=linkOrientations, + linkInertialFramePositions=linkInertialFramePositions, + linkInertialFrameOrientations=linkInertialFrameOrientations, + linkParentIndices=indices, + linkJointTypes=jointTypes, + linkJointAxis=axis, + useMaximalCoordinates=useMaximalCoordinates) + +p.setGravity(0, 0, -10) +p.setRealTimeSimulation(0) + +anistropicFriction = [1, 0.01, 0.01] +p.changeDynamics(sphereUid, -1, lateralFriction=2, anisotropicFriction=anistropicFriction) +p.getNumJoints(sphereUid) +for i in range(p.getNumJoints(sphereUid)): + p.getJointInfo(sphereUid, i) + p.changeDynamics(sphereUid, i, lateralFriction=2, anisotropicFriction=anistropicFriction) + +dt = 1. / 240. +SNAKE_NORMAL_PERIOD = 0.1 #1.5 +m_wavePeriod = SNAKE_NORMAL_PERIOD + +m_waveLength = 4 +m_wavePeriod = 1.5 +m_waveAmplitude = 0.4 +m_waveFront = 0.0 +#our steering value +m_steering = 0.0 +m_segmentLength = sphereRadius * 2.0 +forward = 0 + +while (1): + keys = p.getKeyboardEvents() + for k, v in keys.items(): + + if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_TRIGGERED)): + m_steering = -.2 + if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_RELEASED)): + m_steering = 0 + if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_TRIGGERED)): + m_steering = .2 + if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_RELEASED)): + m_steering = 0 + + amp = 0.2 + offset = 0.6 + numMuscles = p.getNumJoints(sphereUid) + scaleStart = 1.0 + + #start of the snake with smaller waves. + #I think starting the wave at the tail would work better ( while it still goes from head to tail ) + if (m_waveFront < m_segmentLength * 4.0): + scaleStart = m_waveFront / (m_segmentLength * 4.0) + + segment = numMuscles - 1 + + #we simply move a sin wave down the body of the snake. + #this snake may be going backwards, but who can tell ;) + for joint in range(p.getNumJoints(sphereUid)): + segment = joint #numMuscles-1-joint + #map segment to phase + phase = (m_waveFront - (segment + 1) * m_segmentLength) / m_waveLength + phase -= math.floor(phase) + phase *= math.pi * 2.0 + + #map phase to curvature + targetPos = math.sin(phase) * scaleStart * m_waveAmplitude + + #// steer snake by squashing +ve or -ve side of sin curve + if (m_steering > 0 and targetPos < 0): + targetPos *= 1.0 / (1.0 + m_steering) + + if (m_steering < 0 and targetPos > 0): + targetPos *= 1.0 / (1.0 - m_steering) + + #set our motor + p.setJointMotorControl2(sphereUid, + joint, + p.POSITION_CONTROL, + targetPosition=targetPos + m_steering, + force=30) + + #wave keeps track of where the wave is in time + m_waveFront += dt / m_wavePeriod * m_waveLength + p.stepSimulation() + + time.sleep(dt) diff --git a/examples/pybullet/gym/pybullet_examples/soccerball.py b/examples/pybullet/gym/pybullet_examples/soccerball.py new file mode 100644 index 000000000..dae6acd8b --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/soccerball.py @@ -0,0 +1,17 @@ +import pybullet as p +import pybullet_data as pd +import time +p.connect(p.GUI) +p.setAdditionalSearchPath(pd.getDataPath()) +offset = 0 +for scale in range (1,10,1): + ball = p.loadURDF("soccerball.urdf",[offset,0,1], globalScaling=scale*0.1) + p.changeDynamics(ball,-1,linearDamping=0, angularDamping=0, rollingFriction=0.001, spinningFriction=0.001) + p.changeVisualShape(ball,-1,rgbaColor=[0.8,0.8,0.8,1]) + offset += 2*scale*0.1 +p.loadURDF("plane.urdf") +p.setGravity(0,0,-10) +p.setRealTimeSimulation(1) +while p.isConnected(): + #p.getCameraImage(320,200, renderer=p.ER_BULLET_HARDWARE_OPENGL) + time.sleep(0.5) diff --git a/examples/pybullet/gym/pybullet_examples/switchConstraintSolver.py b/examples/pybullet/gym/pybullet_examples/switchConstraintSolver.py new file mode 100644 index 000000000..b526789cc --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/switchConstraintSolver.py @@ -0,0 +1,32 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +#p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001) +p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_DANTZIG, + globalCFM=0.000001) +#p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001) + +p.loadURDF("plane.urdf") +radius = 0.025 +distance = 1.86 +yaw = 135 +pitch = -11 +targetPos = [0, 0, 0] + +p.setPhysicsEngineParameter(solverResidualThreshold=0.001, numSolverIterations=200) +p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos) +objectId = -1 + +for i in range(10): + objectId = p.loadURDF("cube_small.urdf", [1, 1, radius + i * 2 * radius]) + +p.changeDynamics(objectId, -1, 100) + +timeStep = 1. / 240. +p.setGravity(0, 0, -10) +while (p.isConnected()): + p.stepSimulation() + time.sleep(timeStep) diff --git a/examples/pybullet/gym/pybullet_examples/testrender_egl.py b/examples/pybullet/gym/pybullet_examples/testrender_egl.py new file mode 100644 index 000000000..c511e3cdc --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/testrender_egl.py @@ -0,0 +1,102 @@ + +#using the eglRendererPlugin (hardware OpenGL acceleration) +#using EGL on Linux and default OpenGL window on Win32. + +#make sure to compile pybullet with PYBULLET_USE_NUMPY enabled +#otherwise use testrender.py (slower but compatible without numpy) +#you can also use GUI mode, for faster OpenGL rendering (instead of TinyRender CPU) + +import numpy as np +import matplotlib.pyplot as plt +import pybullet +import time +import pkgutil + +plt.ion() + +img = np.random.rand(200, 320) +#img = [tandard_normal((50,100)) +image = plt.imshow(img, interpolation='none', animated=True, label="blah") +ax = plt.gca() +import pybullet_data + + +pybullet.connect(pybullet.DIRECT) + +pybullet.setAdditionalSearchPath(pybullet_data.getDataPath()) + +egl = pkgutil.get_loader('eglRenderer') +if (egl): + pluginId = pybullet.loadPlugin(egl.get_filename(), "_eglRendererPlugin") +else: + pluginId = pybullet.loadPlugin("eglRendererPlugin") +print("pluginId=",pluginId) +pybullet.loadURDF("plane.urdf", [0, 0, -1]) +pybullet.loadURDF("r2d2.urdf") + +camTargetPos = [0, 0, 0] +cameraUp = [0, 0, 1] +cameraPos = [1, 1, 1] +pybullet.setGravity(0, 0, -10) + +pitch = -10.0 + +roll = 0 +upAxisIndex = 2 +camDistance = 4 +pixelWidth = 320 +pixelHeight = 200 +nearPlane = 0.01 +farPlane = 100 + +fov = 60 + +main_start = time.time() +while (1): + for yaw in range(0, 360, 10): + pybullet.stepSimulation() + start = time.time() + + viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, + roll, upAxisIndex) + aspect = pixelWidth / pixelHeight + projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane) + img_arr = pybullet.getCameraImage(pixelWidth, + pixelHeight, + viewMatrix, + projectionMatrix, + shadow=1, + lightDirection=[1, 1, 1], + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + stop = time.time() + #print("renderImage %f" % (stop - start)) + + w = img_arr[0] #width of the image, in pixels + h = img_arr[1] #height of the image, in pixels + rgb = img_arr[2] #color data RGB + dep = img_arr[3] #depth data + + #print('width = %d height = %d' % (w, h)) + + #note that sending the data to matplotlib is really slow + + #reshape is not needed + np_img_arr = np.reshape(rgb, (h, w, 4)) + np_img_arr = np_img_arr * (1. / 255.) + + #show + #plt.imshow(np_img_arr,interpolation='none',extent=(0,1600,0,1200)) + #image = plt.imshow(np_img_arr,interpolation='none',animated=True,label="blah") + + image.set_data(np_img_arr) + ax.plot([0]) + #plt.draw() + #plt.show() + plt.pause(0.01) + #image.draw() + +main_stop = time.time() + +print("Total time %f" % (main_stop - main_start)) + +pybullet.resetSimulation() diff --git a/examples/pybullet/gym/pybullet_examples/testrender_np.py b/examples/pybullet/gym/pybullet_examples/testrender_np.py new file mode 100644 index 000000000..2c313a457 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/testrender_np.py @@ -0,0 +1,90 @@ +#make sure to compile pybullet with PYBULLET_USE_NUMPY enabled +#otherwise use testrender.py (slower but compatible without numpy) +#you can also use GUI mode, for faster OpenGL rendering (instead of TinyRender CPU) + +import numpy as np +import matplotlib.pyplot as plt +import pybullet +import time +import pybullet_data + +plt.ion() + +img = np.random.rand(200, 320) +#img = [tandard_normal((50,100)) +image = plt.imshow(img, interpolation='none', animated=True, label="blah") +ax = plt.gca() + +#pybullet.connect(pybullet.GUI) +pybullet.connect(pybullet.DIRECT) + +pybullet.setAdditionalSearchPath(pybullet_data.getDataPath()) +pybullet.loadURDF("plane.urdf", [0, 0, -1]) +pybullet.loadURDF("r2d2.urdf") + +camTargetPos = [0, 0, 0] +cameraUp = [0, 0, 1] +cameraPos = [1, 1, 1] +pybullet.setGravity(0, 0, -10) + +pitch = -10.0 + +roll = 0 +upAxisIndex = 2 +camDistance = 4 +pixelWidth = 320 +pixelHeight = 200 +nearPlane = 0.01 +farPlane = 100 + +fov = 60 + +main_start = time.time() +while (1): + for yaw in range(0, 360, 10): + pybullet.stepSimulation() + start = time.time() + + viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, + roll, upAxisIndex) + aspect = pixelWidth / pixelHeight + projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane) + img_arr = pybullet.getCameraImage(pixelWidth, + pixelHeight, + viewMatrix, + projectionMatrix, + shadow=1, + lightDirection=[1, 1, 1], + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + stop = time.time() + print("renderImage %f" % (stop - start)) + + w = img_arr[0] #width of the image, in pixels + h = img_arr[1] #height of the image, in pixels + rgb = img_arr[2] #color data RGB + dep = img_arr[3] #depth data + + print('width = %d height = %d' % (w, h)) + + #note that sending the data to matplotlib is really slow + + #reshape is needed + np_img_arr = np.reshape(rgb, (h, w, 4)) + np_img_arr = np_img_arr * (1. / 255.) + + #show + #plt.imshow(np_img_arr,interpolation='none',extent=(0,1600,0,1200)) + #image = plt.imshow(np_img_arr,interpolation='none',animated=True,label="blah") + + image.set_data(np_img_arr) + ax.plot([0]) + #plt.draw() + #plt.show() + plt.pause(0.01) + #image.draw() + +main_stop = time.time() + +print("Total time %f" % (main_stop - main_start)) + +pybullet.resetSimulation() diff --git a/examples/pybullet/gym/pybullet_examples/transparent.py b/examples/pybullet/gym/pybullet_examples/transparent.py new file mode 100644 index 000000000..8a4831002 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/transparent.py @@ -0,0 +1,25 @@ +import pybullet as p +import time +import pybullet_data + +p.connect(p.GUI) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.loadURDF("plane.urdf") +sphereUid = p.loadURDF("sphere_transparent.urdf", [0, 0, 2]) + +redSlider = p.addUserDebugParameter("red", 0, 1, 1) +greenSlider = p.addUserDebugParameter("green", 0, 1, 0) +blueSlider = p.addUserDebugParameter("blue", 0, 1, 0) +alphaSlider = p.addUserDebugParameter("alpha", 0, 1, 0.5) + +while (1): + red = p.readUserDebugParameter(redSlider) + green = p.readUserDebugParameter(greenSlider) + blue = p.readUserDebugParameter(blueSlider) + alpha = p.readUserDebugParameter(alphaSlider) + p.changeVisualShape(sphereUid, -1, rgbaColor=[red, green, blue, alpha]) + p.getCameraImage(320, + 200, + flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_examples/vhacd.py b/examples/pybullet/gym/pybullet_examples/vhacd.py new file mode 100644 index 000000000..8b4bcf77d --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/vhacd.py @@ -0,0 +1,13 @@ +import pybullet as p +import pybullet_data as pd +import os + +import pybullet_data + +p.connect(p.DIRECT) +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +name_in = os.path.join(pd.getDataPath(), "duck.obj") +name_out = "duck_vhacd.obj" +name_log = "log.txt" +p.vhacd(name_in, name_out, name_log) + diff --git a/examples/pybullet/gym/pybullet_examples/video_sync_mp4.py b/examples/pybullet/gym/pybullet_examples/video_sync_mp4.py new file mode 100644 index 000000000..7a517b10e --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/video_sync_mp4.py @@ -0,0 +1,32 @@ +import pybullet as p +import time +import pybullet_data + +#Once the video is recorded, you can extract all individual frames using ffmpeg +#mkdir frames +#ffmpeg -i test.mp4 "frames/out-%03d.png" + +#by default, PyBullet runs at 240Hz +p.connect(p.GUI, options="--width=320 --height=200 --mp4=\"test.mp4\" --mp4fps=240") + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) +p.configureDebugVisualizer(p.COV_ENABLE_GUI,0) +p.configureDebugVisualizer(p.COV_ENABLE_SINGLE_STEP_RENDERING,1) +p.loadURDF("plane.urdf") + +#in 3 seconds, the object travels about 0.5*g*t^2 meter ~ 45 meter. +r2d2 = p.loadURDF("r2d2.urdf",[0,0,45]) +#disable linear damping +p.changeDynamics(r2d2,-1, linearDamping=0) +p.setGravity(0,0,-10) + +for i in range (3*240): + txt = "frame "+str(i) + item = p.addUserDebugText(txt, [0,1,0]) + p.stepSimulation() + #synchronize the visualizer (rendering frames for the video mp4) with stepSimulation + p.configureDebugVisualizer(p.COV_ENABLE_SINGLE_STEP_RENDERING,1) + #print("r2d2 vel=", p.getBaseVelocity(r2d2)[0][2]) + p.removeUserDebugItem(item) + +p.disconnect() diff --git a/examples/pybullet/gym/pybullet_examples/vr_kuka_setup.py b/examples/pybullet/gym/pybullet_examples/vr_kuka_setup.py new file mode 100644 index 000000000..ee94d5f30 --- /dev/null +++ b/examples/pybullet/gym/pybullet_examples/vr_kuka_setup.py @@ -0,0 +1,184 @@ +import pybullet as p +import time +#p.connect(p.UDP,"192.168.86.100") +import pybullet_data + +cid = p.connect(p.SHARED_MEMORY) + +if (cid < 0): + p.connect(p.GUI) + +p.setAdditionalSearchPath(pybullet_data.getDataPath()) + +p.resetSimulation() +#disable rendering during loading makes it much faster +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) +objects = [ + p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000) +] +objects = [ + p.loadURDF("samurai.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031, + 1.000000) +] +pr2_gripper = objects[0] +print("pr2_gripper=") +print(pr2_gripper) + +jointPositions = [0.550569, 0.000000, 0.549657, 0.000000] +for jointIndex in range(p.getNumJoints(pr2_gripper)): + p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex]) + +pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0], + [0.500000, 0.300006, 0.700000]) +print("pr2_cid") +print(pr2_cid) + +objects = [ + p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000, + 0.000000, 1.000000) +] +kuka = objects[0] +jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001] +for jointIndex in range(p.getNumJoints(kuka)): + p.resetJointState(kuka, jointIndex, jointPositions[jointIndex]) + p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0) + +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf") +kuka_gripper = objects[0] +print("kuka gripper=") +print(kuka_gripper) + +p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036], + [-0.000000, 0.964531, -0.000002, -0.263970]) +jointPositions = [ + 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 +] +for jointIndex in range(p.getNumJoints(kuka_gripper)): + p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex]) + p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], + 0) + +kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05], + [0, 0, 0]) + +pr2_cid2 = p.createConstraint(kuka_gripper, + 4, + kuka_gripper, + 6, + jointType=p.JOINT_GEAR, + jointAxis=[1, 1, 1], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) +p.changeConstraint(pr2_cid2, gearRatio=-1, erp=0.5, relativePositionTarget=0, maxForce=100) + +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = p.loadSDF("kiva_shelf/model.sdf") +ob = objects[0] +p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500], + [0.000000, 0.000000, 0.000000, 1.000000]) +objects = [ + p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000, + 0.773962) +] +objects = [ + p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 1.000000) +] +ob = objects[0] +jointPositions = [0.000000] +for jointIndex in range(p.getNumJoints(ob)): + p.resetJointState(ob, jointIndex, jointPositions[jointIndex]) + +objects = [ + p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +ob = objects[0] +jointPositions = [ + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000 +] +for jointIndex in range(p.getNumJoints(ob)): + p.resetJointState(ob, jointIndex, jointPositions[jointIndex]) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) + +p.setGravity(0.000000, 0.000000, 0.000000) +p.setGravity(0, 0, -10) + +##show this for 10 seconds +#now = time.time() +#while (time.time() < now+10): +# p.stepSimulation() +p.setRealTimeSimulation(1) + +while (1): + p.setGravity(0, 0, -10) +p.disconnect() diff --git a/setup.py b/setup.py index 84d61f329..ef2748b4a 100644 --- a/setup.py +++ b/setup.py @@ -459,7 +459,7 @@ hh = setup_py_dir + "/" + datadir for root, dirs, files in os.walk(hh): for fn in files: ext = os.path.splitext(fn)[1][1:] - if ext and ext in 'yaml index meta data-00000-of-00001 png gif jpg urdf sdf obj txt mtl dae off stl STL xml '.split( + if ext and ext in 'yaml index meta data-00000-of-00001 png gif jpg urdf sdf obj txt mtl dae off stl STL xml gin npy '.split( ): fn = root + "/" + fn need_files.append(fn[1 + len(hh):]) From 5205dafcc2cf4bb6eb094992717918053b40b7e5 Mon Sep 17 00:00:00 2001 From: Erwin Coumans Date: Mon, 5 Apr 2021 19:51:38 -0700 Subject: [PATCH 34/36] add __init__.py --- examples/pybullet/examples/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/pybullet/examples/__init__.py diff --git a/examples/pybullet/examples/__init__.py b/examples/pybullet/examples/__init__.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/examples/pybullet/examples/__init__.py @@ -0,0 +1 @@ + From dcf9c8d6a5c4c76ade7823895d286b397c66b49b Mon Sep 17 00:00:00 2001 From: jaraujo98 Date: Tue, 6 Apr 2021 11:26:39 -0700 Subject: [PATCH 35/36] Replace xrange with range The xrange function was deprecated in Python 3. Removing it for compatibility. --- .../minitaur/envs/env_randomizers/minitaur_env_randomizer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py index 0fbfef36d..81b89f301 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py @@ -56,7 +56,7 @@ class MinitaurEnvRandomizer(env_randomizer_base.EnvRandomizerBase): leg_masses_upper_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[1]) randomized_leg_masses = [ np.random.uniform(leg_masses_lower_bound[i], leg_masses_upper_bound[i]) - for i in xrange(len(leg_masses)) + for i in range(len(leg_masses)) ] minitaur.SetLegMasses(randomized_leg_masses) From 0a8e4f3f6c31555f6acfaefc41614c5312241539 Mon Sep 17 00:00:00 2001 From: erwincoumans Date: Sat, 10 Apr 2021 15:56:13 -0700 Subject: [PATCH 36/36] Update README.md --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index e0fb2af97..4aec06f86 100644 --- a/README.md +++ b/README.md @@ -7,8 +7,11 @@ This is the official C++ source code repository of the Bullet Physics SDK: real- ![PyBullet](https://pybullet.org/wordpress/wp-content/uploads/2019/03/cropped-pybullet.png) +## Issues ## +The Issue tracker was flooded with support questions and is closed until it is cleaned up. Use the [PyBullet forums](http://pybullet.org) to discuss with others. + ## PyBullet ## -New in Bullet 2.85: pybullet Python bindings, improved support for robotics and VR. Use pip install pybullet and checkout the [PyBullet Quickstart Guide](https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.2ye70wns7io3). +It is highly recommended to use PyBullet Python bindings for improved support for robotics, reinforcement learning and VR. Use pip install pybullet and checkout the [PyBullet Quickstart Guide](https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.2ye70wns7io3). Installation is simple: ```

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