mirror of
https://github.com/bulletphysics/bullet3
synced 2025-01-18 21:10:05 +00:00
Merge branch 'master' into SIblock
This commit is contained in:
erwincoumans
GitHub
commit
4d3f2e5b17
@ -109,9 +109,9 @@ int DillCreator::recurseDill(const int level, const int parent, const idScalar d
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m_body_T_parent_ref[body](i, j) = 0.0;
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}
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}
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const idScalar size_5 = pow(size, 5);
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m_body_I_body[body](0, 0) = size_5 / 0.2e6;
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m_body_I_body[body](1, 1) = size_5 * 403 / 1.2e6;
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const idScalar size_5 = std::pow(size, 5);
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m_body_I_body[body](0, 0) = size_5 / 0.2e6;
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m_body_I_body[body](1, 1) = size_5 * 403 / 1.2e6;
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m_body_I_body[body](2, 2) = m_body_I_body[body](1, 1);
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getVecMatFromDH(0, 0, a_DH_in, alpha_DH_in, &m_parent_r_parent_body_ref[body],
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@ -19,6 +19,7 @@ subject to the following restrictions:
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#include "LinearMath/btIDebugDraw.h"
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#include <stdio.h> //printf debugging
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#include <cmath>
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#include "../CommonInterfaces/CommonRigidBodyBase.h"
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@ -114,8 +115,8 @@ void AllConstraintDemo::initPhysics()
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///gear constraint demo
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#define THETA SIMD_PI / 4.f
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#define L_1 (2 - tan(THETA))
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#define L_2 (1 / cos(THETA))
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#define L_1 (2 - std::tan(THETA))
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#define L_2 (1 / std::cos(THETA))
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#define RATIO L_2 / L_1
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btRigidBody* bodyA = 0;
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@ -5,11 +5,6 @@
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#include "../BlockSolver/RigidBodyBoxes.h"
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#include "LinearMath/btAlignedObjectArray.h"
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#include "EmptyExample.h"
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#include "../BulletRobotics/BoxStack.h"
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#include "../BulletRobotics/FixJointBoxes.h"
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#include "../BulletRobotics/JointLimit.h"
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// #include "../BulletRobotics/GraspBox.h"
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#include "../BulletRobotics/FixJointBoxes.h"
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#include "../RenderingExamples/RenderInstancingDemo.h"
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#include "../RenderingExamples/CoordinateSystemDemo.h"
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#include "../RenderingExamples/RaytracerSetup.h"
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@ -136,12 +131,6 @@ static ExampleEntry gDefaultExamples[] =
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ExampleEntry(1, "Soft Contact", "Using the error correction parameter (ERP) and constraint force mixing (CFM) values for contacts to simulate compliant contact.", RigidBodySoftContactCreateFunc),
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ExampleEntry(0, "Bullet Robotics"),
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ExampleEntry(1, "Box Stack", "Create a stack of boxes of large mass ratio.", BoxStackExampleCreateFunc),
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ExampleEntry(1, "Joint Limit", "Create three objects joint together", JointLimitCreateFunc),
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// ExampleEntry(1, "Grasp Box", "A robot arm of large mass tries to grasp a box of small mass", GraspBoxCreateFunc),
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ExampleEntry(1, "FixJoint Boxes", "FixJoint Boxes", FixJointBoxesCreateFunc),
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ExampleEntry(0, "MultiBody"),
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ExampleEntry(1, "MultiDof", "Create a basic btMultiBody with 3-DOF spherical joints (mobilizers). The demo uses a fixed base or a floating base at restart.", MultiDofCreateFunc),
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ExampleEntry(1, "TestJointTorque", "Apply a torque to a btMultiBody with 1-DOF joints (mobilizers). This setup is similar to API/TestHingeTorque.", TestJointTorqueCreateFunc),
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@ -21,6 +21,12 @@ inline char* strDup(const char* const str)
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template <typename T, typename U>
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void addJointInfoFromMultiBodyData(const T* mb, U* bodyJoints, bool verboseOutput)
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{
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int numDofs = 0;
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if (mb->m_baseMass>0)
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{
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numDofs = 6;
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}
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if (mb->m_baseName)
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{
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if (verboseOutput)
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@ -107,7 +113,9 @@ void addJointInfoFromMultiBodyData(const T* mb, U* bodyJoints, bool verboseOutpu
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}
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qOffset += mb->m_links[link].m_posVarCount;
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uOffset += mb->m_links[link].m_dofCount;
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numDofs += mb->m_links[link].m_dofCount;
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}
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bodyJoints->m_numDofs = numDofs;
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}
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#endif //BODY_JOINT_INFO_UTILITY_H
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@ -33,6 +33,8 @@ public:
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virtual int getNumJoints(int bodyUniqueId) const = 0;
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virtual int getNumDofs(int bodyUniqueId) const = 0;
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virtual bool getJointInfo(int bodyUniqueId, int jointIndex, struct b3JointInfo& info) const = 0;
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virtual int getNumUserConstraints() const = 0;
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@ -2526,6 +2526,12 @@ B3_SHARED_API int b3GetNumJoints(b3PhysicsClientHandle physClient, int bodyUniqu
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return cl->getNumJoints(bodyUniqueId);
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}
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B3_SHARED_API int b3GetNumDofs(b3PhysicsClientHandle physClient, int bodyUniqueId)
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{
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PhysicsClient* cl = (PhysicsClient*)physClient;
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return cl->getNumDofs(bodyUniqueId);
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}
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B3_SHARED_API int b3ComputeDofCount(b3PhysicsClientHandle physClient, int bodyUniqueId)
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{
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int nj = b3GetNumJoints(physClient, bodyUniqueId);
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@ -4642,8 +4648,9 @@ B3_SHARED_API b3SharedMemoryCommandHandle b3CalculateJacobianCommandInit(b3Physi
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command->m_calculateJacobianArguments.m_localPosition[0] = localPosition[0];
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command->m_calculateJacobianArguments.m_localPosition[1] = localPosition[1];
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command->m_calculateJacobianArguments.m_localPosition[2] = localPosition[2];
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int numJoints = cl->getNumJoints(bodyUniqueId);
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for (int i = 0; i < numJoints; i++)
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int numDofs = cl->getNumDofs(bodyUniqueId);
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for (int i = 0; i < numDofs; i++)
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{
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command->m_calculateJacobianArguments.m_jointPositionsQ[i] = jointPositionsQ[i];
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command->m_calculateJacobianArguments.m_jointVelocitiesQdot[i] = jointVelocitiesQdot[i];
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@ -118,7 +118,9 @@ extern "C"
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///give a unique body index (after loading the body) return the number of joints.
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B3_SHARED_API int b3GetNumJoints(b3PhysicsClientHandle physClient, int bodyUniqueId);
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///give a unique body index (after loading the body) return the number of degrees of freedom (DoF).
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B3_SHARED_API int b3GetNumDofs(b3PhysicsClientHandle physClient, int bodyUniqueId);
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///compute the number of degrees of freedom for this body.
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///Return -1 for unsupported spherical joint, -2 for unsupported planar joint.
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B3_SHARED_API int b3ComputeDofCount(b3PhysicsClientHandle physClient, int bodyUniqueId);
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@ -20,7 +20,7 @@ struct BodyJointInfoCache
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b3AlignedObjectArray<b3JointInfo> m_jointInfo;
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std::string m_bodyName;
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btAlignedObjectArray<int> m_userDataIds;
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int m_numDofs;
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~BodyJointInfoCache()
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{
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}
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@ -144,6 +144,17 @@ int PhysicsClientSharedMemory::getNumJoints(int bodyUniqueId) const
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return 0;
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}
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int PhysicsClientSharedMemory::getNumDofs(int bodyUniqueId) const
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{
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BodyJointInfoCache** bodyJointsPtr = m_data->m_bodyJointMap[bodyUniqueId];
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if (bodyJointsPtr && *bodyJointsPtr)
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{
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BodyJointInfoCache* bodyJoints = *bodyJointsPtr;
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return bodyJoints->m_numDofs;
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}
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return 0;
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}
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bool PhysicsClientSharedMemory::getJointInfo(int bodyUniqueId, int jointIndex, b3JointInfo& info) const
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{
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BodyJointInfoCache** bodyJointsPtr = m_data->m_bodyJointMap[bodyUniqueId];
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@ -45,6 +45,8 @@ public:
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virtual int getNumJoints(int bodyUniqueId) const;
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virtual int getNumDofs(int bodyUniqueId) const;
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virtual bool getJointInfo(int bodyUniqueId, int jointIndex, struct b3JointInfo& info) const;
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virtual int getNumUserConstraints() const;
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@ -22,6 +22,7 @@ struct BodyJointInfoCache2
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btAlignedObjectArray<b3JointInfo> m_jointInfo;
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std::string m_bodyName;
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btAlignedObjectArray<int> m_userDataIds;
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int m_numDofs;
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~BodyJointInfoCache2()
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{
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@ -1275,9 +1276,9 @@ bool PhysicsDirect::getBodyInfo(int bodyUniqueId, struct b3BodyInfo& info) const
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return false;
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}
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int PhysicsDirect::getNumJoints(int bodyIndex) const
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int PhysicsDirect::getNumJoints(int bodyUniqueId) const
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{
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BodyJointInfoCache2** bodyJointsPtr = m_data->m_bodyJointMap[bodyIndex];
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BodyJointInfoCache2** bodyJointsPtr = m_data->m_bodyJointMap[bodyUniqueId];
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if (bodyJointsPtr && *bodyJointsPtr)
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{
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BodyJointInfoCache2* bodyJoints = *bodyJointsPtr;
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@ -1287,6 +1288,18 @@ int PhysicsDirect::getNumJoints(int bodyIndex) const
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return 0;
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}
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int PhysicsDirect::getNumDofs(int bodyUniqueId) const
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{
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BodyJointInfoCache2** bodyJointsPtr = m_data->m_bodyJointMap[bodyUniqueId];
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if (bodyJointsPtr && *bodyJointsPtr)
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{
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BodyJointInfoCache2* bodyJoints = *bodyJointsPtr;
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return bodyJoints->m_numDofs;
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}
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btAssert(0);
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return 0;
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}
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bool PhysicsDirect::getJointInfo(int bodyIndex, int jointIndex, struct b3JointInfo& info) const
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{
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BodyJointInfoCache2** bodyJointsPtr = m_data->m_bodyJointMap[bodyIndex];
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@ -61,7 +61,9 @@ public:
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virtual bool getBodyInfo(int bodyUniqueId, struct b3BodyInfo& info) const;
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virtual int getNumJoints(int bodyIndex) const;
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virtual int getNumJoints(int bodyUniqueId) const;
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virtual int getNumDofs(int bodyUniqueId) const;
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virtual bool getJointInfo(int bodyIndex, int jointIndex, struct b3JointInfo& info) const;
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@ -107,9 +107,14 @@ bool PhysicsLoopBack::getBodyInfo(int bodyUniqueId, struct b3BodyInfo& info) con
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return m_data->m_physicsClient->getBodyInfo(bodyUniqueId, info);
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}
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int PhysicsLoopBack::getNumJoints(int bodyIndex) const
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int PhysicsLoopBack::getNumJoints(int bodyUniqueId) const
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{
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return m_data->m_physicsClient->getNumJoints(bodyIndex);
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return m_data->m_physicsClient->getNumJoints(bodyUniqueId);
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}
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int PhysicsLoopBack::getNumDofs(int bodyUniqueId) const
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{
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return m_data->m_physicsClient->getNumDofs(bodyUniqueId);
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}
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bool PhysicsLoopBack::getJointInfo(int bodyIndex, int jointIndex, struct b3JointInfo& info) const
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@ -42,7 +42,9 @@ public:
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virtual bool getBodyInfo(int bodyUniqueId, struct b3BodyInfo& info) const;
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virtual int getNumJoints(int bodyIndex) const;
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virtual int getNumJoints(int bodyUniqueId) const;
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virtual int getNumDofs(int bodyUniqueId) const;
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virtual bool getJointInfo(int bodyIndex, int jointIndex, struct b3JointInfo& info) const;
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@ -1699,6 +1699,7 @@ struct PhysicsServerCommandProcessorInternalData
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m_logPlaybackUid(-1),
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m_physicsDeltaTime(1. / 240.),
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m_numSimulationSubSteps(0),
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m_simulationTimestamp(0),
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m_userConstraintUIDGenerator(1),
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m_broadphaseCollisionFilterCallback(0),
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m_pairCache(0),
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@ -5,8 +5,9 @@
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*/
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#include "Gwen/Anim.h"
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#include "Gwen/Utility.h"
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#include <math.h>
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#include <cmath>
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#include "Gwen/Utility.h"
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using namespace Gwen;
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@ -123,13 +124,12 @@ void Gwen::Anim::TimedAnimation::Think()
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if (fDelta < 0.0f) fDelta = 0.0f;
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if (fDelta > 1.0f) fDelta = 1.0f;
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Run(pow(fDelta, m_fEase));
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Run(std::pow(fDelta, m_fEase));
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if (fDelta == 1.0f)
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{
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m_bFinished = true;
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OnFinish();
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}
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if (fDelta == 1.0f) {
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m_bFinished = true;
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OnFinish();
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}
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}
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bool Gwen::Anim::TimedAnimation::Finished()
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@ -11,6 +11,7 @@
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#include "Gwen/Platform.h"
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#include <math.h>
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#include <cmath>
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using namespace Gwen;
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using namespace Gwen::Controls;
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@ -89,9 +90,10 @@ void TextBox::Render(Skin::Base* skin)
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}
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// Draw caret
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if (fmod(Gwen::Platform::GetTimeInSeconds() - m_fLastInputTime, 1.0f) > 0.5f)
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skin->GetRender()->SetDrawColor(Gwen::Color(255, 255, 255, 255));
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else
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if (std::fmod(Gwen::Platform::GetTimeInSeconds() - m_fLastInputTime,
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1.0f) > 0.5f)
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skin->GetRender()->SetDrawColor(Gwen::Color(255, 255, 255, 255));
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else
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skin->GetRender()->SetDrawColor(Gwen::Color(0, 0, 0, 255));
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skin->GetRender()->DrawFilledRect(m_rectCaretBounds);
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@ -1,20 +1,21 @@
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#include "TinyRenderer.h"
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#include <vector>
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#include <limits>
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#include <cmath>
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#include <iostream>
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#include "tgaimage.h"
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#include "model.h"
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#include "geometry.h"
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#include "our_gl.h"
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#include "../Utils/b3ResourcePath.h"
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#include "Bullet3Common/b3MinMax.h"
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#include <limits>
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#include <vector>
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#include "../CommonInterfaces/CommonFileIOInterface.h"
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#include "../OpenGLWindow/ShapeData.h"
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#include "../Utils/b3BulletDefaultFileIO.h"
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#include "../Utils/b3ResourcePath.h"
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#include "Bullet3Common/b3Logging.h"
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#include "Bullet3Common/b3MinMax.h"
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#include "LinearMath/btAlignedObjectArray.h"
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#include "LinearMath/btVector3.h"
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#include "Bullet3Common/b3Logging.h"
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#include "../CommonInterfaces/CommonFileIOInterface.h"
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#include "../Utils/b3BulletDefaultFileIO.h"
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#include "geometry.h"
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#include "model.h"
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#include "our_gl.h"
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#include "tgaimage.h"
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struct DepthShader : public IShader
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{
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@ -161,10 +162,11 @@ struct Shader : public IShader
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Vec2f uv = varying_uv * bar;
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|
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Vec3f reflection_direction = (bn * (bn * m_light_dir_local * 2.f) - m_light_dir_local).normalize();
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float specular = pow(b3Max(reflection_direction.z, 0.f), m_model->specular(uv));
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float diffuse = b3Max(0.f, bn * m_light_dir_local);
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float specular = std::pow(b3Max(reflection_direction.z, 0.f),
|
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m_model->specular(uv));
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float diffuse = b3Max(0.f, bn * m_light_dir_local);
|
||||
|
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color = m_model->diffuse(uv);
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color = m_model->diffuse(uv);
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color[0] *= m_colorRGBA[0];
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color[1] *= m_colorRGBA[1];
|
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color[2] *= m_colorRGBA[2];
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
#include "model.h"
|
||||
#include "Bullet3Common/b3Logging.h"
|
||||
#include <string.h> // memcpy
|
||||
#include <cmath>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include "Bullet3Common/b3Logging.h"
|
||||
Model::Model(const char *filename) : verts_(), faces_(), norms_(), uv_(), diffusemap_(), normalmap_(), specularmap_()
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||||
{
|
||||
std::ifstream in;
|
||||
@ -159,10 +160,10 @@ TGAColor Model::diffuse(Vec2f uvf)
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||||
// bool repeat = true;
|
||||
// if (repeat)
|
||||
{
|
||||
uvf[0] = modf(uvf[0], &val);
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||||
uvf[1] = modf(uvf[1], &val);
|
||||
}
|
||||
Vec2i uv(uvf[0] * diffusemap_.get_width(), uvf[1] * diffusemap_.get_height());
|
||||
uvf[0] = std::modf(uvf[0], &val);
|
||||
uvf[1] = std::modf(uvf[1], &val);
|
||||
}
|
||||
Vec2i uv(uvf[0] * diffusemap_.get_width(), uvf[1] * diffusemap_.get_height());
|
||||
return diffusemap_.get(uv[0], uv[1]);
|
||||
}
|
||||
return TGAColor(255, 255, 255, 255);
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
import pybullet as p
|
||||
import os
|
||||
|
||||
def getDataPath():
|
||||
|
||||
@ -25,14 +25,14 @@ register(
|
||||
register(
|
||||
id='MinitaurBulletEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:MinitaurBulletEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=15.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='MinitaurBulletDuckEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:MinitaurBulletDuckEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
@ -40,7 +40,7 @@ register(
|
||||
register(
|
||||
id='MinitaurReactiveEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurReactiveEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
@ -48,7 +48,7 @@ register(
|
||||
register(
|
||||
id='MinitaurBallGymEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurBallGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
@ -56,28 +56,28 @@ register(
|
||||
register(
|
||||
id='MinitaurTrottingEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurTrottingEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='MinitaurStandGymEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurStandGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='MinitaurAlternatingLegsEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurAlternatingLegsEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='MinitaurFourLegStandEnv-v0',
|
||||
entry_point='pybullet_envs.minitaur.envs:MinitaurFourLegStandEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
@ -86,14 +86,14 @@ register(
|
||||
register(
|
||||
id='RacecarBulletEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:RacecarGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='RacecarZedBulletEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:RacecarZEDGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
@ -101,21 +101,21 @@ register(
|
||||
register(
|
||||
id='KukaBulletEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:KukaGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='KukaCamBulletEnv-v0',
|
||||
entry_point='pybullet_envs.bullet:KukaCamGymEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
register(
|
||||
id='KukaDiverseObjectGrasping-v0',
|
||||
entry_point='pybullet_envs.bullet:KukaDiverseObjectEnv',
|
||||
timestep_limit=1000,
|
||||
max_episode_steps=1000,
|
||||
reward_threshold=5.0,
|
||||
)
|
||||
|
||||
|
||||
@ -149,7 +149,7 @@ def save_config(config, logdir=None):
|
||||
tf.logging.info(message.format(config.logdir))
|
||||
tf.gfile.MakeDirs(config.logdir)
|
||||
config_path = os.path.join(config.logdir, 'config.yaml')
|
||||
with tf.gfile.FastGFile(config_path, 'w') as file_:
|
||||
with tf.gfile.GFile(config_path, 'w') as file_:
|
||||
yaml.dump(config, file_, default_flow_style=False)
|
||||
else:
|
||||
message = (
|
||||
|
||||
@ -0,0 +1,2 @@
|
||||
|
||||
|
||||
@ -0,0 +1,70 @@
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
from absl import app
|
||||
from absl import flags
|
||||
from keras.callbacks import ModelCheckpoint
|
||||
from keras.layers import Dense
|
||||
from keras.models import Sequential
|
||||
from keras import optimizers
|
||||
import numpy
|
||||
|
||||
FLAGS = flags.FLAGS
|
||||
|
||||
flags.DEFINE_string(
|
||||
"input_filename", "data/minitaur_log_latency_0.01.csv", "The name of the input CSV file."
|
||||
"Each line in the CSV file will contain the motor position, the "
|
||||
"motor speed, action and torques.")
|
||||
|
||||
|
||||
def main(unused_argv):
|
||||
# fix random seed for reproducibility
|
||||
numpy.random.seed(7)
|
||||
# load pima indians dataset
|
||||
dataset = numpy.loadtxt(
|
||||
FLAGS.input_filename,
|
||||
delimiter=",")
|
||||
# split into input (X) and output (Y) variables
|
||||
x = dataset[:, 0:3]
|
||||
y = dataset[:, 3]
|
||||
print("x=", x)
|
||||
print("y=", y)
|
||||
|
||||
# create model
|
||||
model = Sequential()
|
||||
model.add(Dense(12, input_dim=3, activation="relu"))
|
||||
model.add(Dense(8, activation="sigmoid"))
|
||||
model.add(Dense(1, activation="linear"))
|
||||
|
||||
# Compile model (use adam or sgd)
|
||||
model.compile(
|
||||
loss="mean_squared_error",
|
||||
optimizer="adam",
|
||||
metrics=["mean_squared_error"])
|
||||
|
||||
# checkpoint
|
||||
filepath = "/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath, monitor="val_loss", verbose=1, save_best_only=True, mode="min")
|
||||
callbacks_list = [checkpoint]
|
||||
|
||||
# Fit the model
|
||||
# model.fit(X, Y, epochs=150, batch_size=10)
|
||||
# model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list)
|
||||
model.fit(
|
||||
x,
|
||||
y,
|
||||
validation_split=0.34,
|
||||
epochs=4500,
|
||||
batch_size=1024,
|
||||
callbacks=callbacks_list,
|
||||
verbose=0)
|
||||
|
||||
# evaluate the model
|
||||
scores = model.evaluate(x, y)
|
||||
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(main)
|
||||
@ -0,0 +1,166 @@
|
||||
"""see https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/"""
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use('TkAgg')
|
||||
import numpy as np
|
||||
from matplotlib import pyplot
|
||||
from pandas import read_csv
|
||||
from pandas import DataFrame
|
||||
from pandas import concat
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Dropout
|
||||
from keras.layers import Dense
|
||||
from keras.layers import LSTM
|
||||
from keras.callbacks import ModelCheckpoint
|
||||
from matplotlib import pyplot
|
||||
|
||||
from pandas import read_csv
|
||||
|
||||
|
||||
# convert series to supervised learning
|
||||
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
|
||||
n_vars = 1 if type(data) is list else data.shape[1]
|
||||
df = DataFrame(data)
|
||||
cols, names = list(), list()
|
||||
# input sequence (t-n, ... t-1)
|
||||
for i in range(n_in, 0, -1):
|
||||
cols.append(df.shift(i))
|
||||
names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
|
||||
# forecast sequence (t, t+1, ... t+n)
|
||||
for i in range(0, n_out):
|
||||
cols.append(df.shift(-i))
|
||||
if i == 0:
|
||||
names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
|
||||
else:
|
||||
names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
|
||||
# put it all together
|
||||
agg = concat(cols, axis=1)
|
||||
agg.columns = names
|
||||
# drop rows with NaN values
|
||||
if dropnan:
|
||||
agg.dropna(inplace=True)
|
||||
return agg
|
||||
|
||||
|
||||
values = [x for x in range(10)]
|
||||
data = series_to_supervised(values, 2)
|
||||
print(data)
|
||||
|
||||
# load dataset
|
||||
#dataset = read_csv('./data/minitaur_log_latency_0.01.csv')
|
||||
#dataset = read_csv('./data/minitaur_log_latency_0.003.csv')
|
||||
dataset = read_csv('./data/minitaur_log_latency_0.006.csv')
|
||||
|
||||
values = dataset.values
|
||||
# integer encode direction
|
||||
#encoder = LabelEncoder()
|
||||
#values[:,3] = encoder.fit_transform(values[:,3])
|
||||
# ensure all data is float
|
||||
values = values.astype('float32')
|
||||
|
||||
# normalize features
|
||||
useNormalization = False
|
||||
if useNormalization:
|
||||
scaler = MinMaxScaler(feature_range=(0, 1))
|
||||
scaled = scaler.fit_transform(values)
|
||||
else:
|
||||
scaled = values
|
||||
|
||||
# frame as supervised learning
|
||||
lag_steps = 5
|
||||
reframed = series_to_supervised(scaled, lag_steps, 1)
|
||||
print("reframed before drop=", reframed)
|
||||
|
||||
# drop columns we don't want to predict
|
||||
reframed.drop(reframed.columns[[3,7,11,15,19]], axis=1, inplace=True)
|
||||
print("after drop=",reframed.head())
|
||||
|
||||
#dummy = scaler.inverse_transform(reframed)
|
||||
#print(dummy)
|
||||
|
||||
groups = [0, 1, 2, 3]
|
||||
|
||||
i = 1
|
||||
# plot each column
|
||||
doPlot = False
|
||||
if doPlot:
|
||||
pyplot.figure()
|
||||
for group in groups:
|
||||
pyplot.subplot(len(groups), 1, i)
|
||||
pyplot.plot(values[0:25, group])
|
||||
pyplot.title(dataset.columns[group], y=0.5, loc='right')
|
||||
i += 1
|
||||
pyplot.show()
|
||||
|
||||
# split into train and test sets
|
||||
values = reframed.values
|
||||
n_train_hours = 6000
|
||||
train = values[:n_train_hours, :]
|
||||
test = values[n_train_hours:, :]
|
||||
# split into input and outputs
|
||||
train_X, train_y = train[:, :-1], train[:, -1]
|
||||
test_X, test_y = test[:, :-1], test[:, -1]
|
||||
|
||||
print("train_X.shape[1]=",train_X.shape[1])
|
||||
|
||||
|
||||
# design network
|
||||
useLSTM=True
|
||||
if useLSTM:
|
||||
# reshape input to be 3D [samples, timesteps, features]
|
||||
train_X = train_X.reshape((train_X.shape[0], lag_steps+1, int(train_X.shape[1]/(lag_steps+1))))
|
||||
test_X = test_X.reshape((test_X.shape[0], lag_steps+1, int(test_X.shape[1]/(lag_steps+1))))
|
||||
model = Sequential()
|
||||
model.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2])))
|
||||
model.add(Dropout(0.05))
|
||||
model.add(Dense(8, activation='sigmoid'))
|
||||
model.add(Dense(8, activation='sigmoid'))
|
||||
model.add(Dropout(0.05))
|
||||
model.add(Dense(1, activation='linear'))
|
||||
else:
|
||||
# create model
|
||||
model = Sequential()
|
||||
model.add(Dense(12, input_dim=train_X.shape[1], activation="relu"))
|
||||
model.add(Dense(8, activation="sigmoid"))
|
||||
model.add(Dense(1, activation="linear"))
|
||||
|
||||
#model.compile(loss='mae', optimizer='adam')
|
||||
model.compile(
|
||||
loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error'])
|
||||
|
||||
# checkpoint
|
||||
filepath = '/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5'
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
|
||||
callbacks_list = [checkpoint]
|
||||
|
||||
# fit network
|
||||
history = model.fit(
|
||||
train_X,
|
||||
train_y,
|
||||
epochs=1500,
|
||||
batch_size=32,
|
||||
callbacks=callbacks_list,
|
||||
validation_data=(test_X, test_y),
|
||||
verbose=2,
|
||||
shuffle=False)
|
||||
# plot history
|
||||
|
||||
|
||||
data = np.array([[[1.513535008329887299,3.234624992847829894e-01,1.731481043119239782,1.741165415165205399,
|
||||
1.534267104753672228e+00,1.071354965017878635e+00,1.712386127673626302e+00]]])
|
||||
|
||||
|
||||
#prediction = model.predict(data)
|
||||
#print("prediction=",prediction)
|
||||
|
||||
|
||||
pyplot.plot(history.history['loss'], label='train')
|
||||
pyplot.plot(history.history['val_loss'], label='test')
|
||||
pyplot.legend()
|
||||
pyplot.show()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
@ -0,0 +1,67 @@
|
||||
#The example to run the raibert controller in a Minitaur gym env.
|
||||
|
||||
#blaze run :minitaur_raibert_controller_example -- --log_path=/tmp/logs
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import tensorflow as tf
|
||||
from pybullet_envs.minitaur.envs import minitaur_raibert_controller
|
||||
from pybullet_envs.minitaur.envs import minitaur_gym_env
|
||||
|
||||
flags = tf.app.flags
|
||||
FLAGS = tf.app.flags.FLAGS
|
||||
|
||||
flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.")
|
||||
flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.")
|
||||
flags.DEFINE_float(
|
||||
"control_latency", 0.006, "The latency between sensor measurement and action"
|
||||
" execution the robot.")
|
||||
flags.DEFINE_string("log_path", ".", "The directory to write the log file.")
|
||||
|
||||
|
||||
def speed(t):
|
||||
max_speed = 0.35
|
||||
t1 = 3
|
||||
if t < t1:
|
||||
return t / t1 * max_speed
|
||||
else:
|
||||
return -max_speed
|
||||
|
||||
|
||||
def main(argv):
|
||||
del argv
|
||||
env = minitaur_gym_env.MinitaurGymEnv(
|
||||
urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
|
||||
control_time_step=0.006,
|
||||
action_repeat=6,
|
||||
pd_latency=0.0,
|
||||
control_latency=FLAGS.control_latency,
|
||||
motor_kp=FLAGS.motor_kp,
|
||||
motor_kd=FLAGS.motor_kd,
|
||||
remove_default_joint_damping=True,
|
||||
leg_model_enabled=False,
|
||||
render=True,
|
||||
on_rack=False,
|
||||
accurate_motor_model_enabled=True,
|
||||
log_path=FLAGS.log_path)
|
||||
env.reset()
|
||||
|
||||
controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(
|
||||
env.minitaur)
|
||||
|
||||
tstart = env.minitaur.GetTimeSinceReset()
|
||||
for _ in range(1000):
|
||||
t = env.minitaur.GetTimeSinceReset() - tstart
|
||||
controller.behavior_parameters = (
|
||||
minitaur_raibert_controller.BehaviorParameters(
|
||||
desired_forward_speed=speed(t)))
|
||||
controller.update(t)
|
||||
env.step(controller.get_action())
|
||||
|
||||
#env.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
tf.app.run(main)
|
||||
|
||||
@ -0,0 +1,46 @@
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
#python proto2csv.py --proto_file=/tmp/logs/minitaur_log_2019-01-27-12-59-31 --csv_file=/tmp/logs/out.csv
|
||||
#each line in csv contains: angle, velocity, action, torque
|
||||
|
||||
import tensorflow as tf
|
||||
import argparse
|
||||
import numpy
|
||||
from pybullet_envs.minitaur.envs import minitaur_logging
|
||||
|
||||
flags = tf.app.flags
|
||||
FLAGS = tf.app.flags.FLAGS
|
||||
|
||||
flags.DEFINE_string("proto_file", "logs", "path to protobuf input file")
|
||||
flags.DEFINE_string("csv_file", "file.csv", "poth to csv output file")
|
||||
|
||||
|
||||
def main(argv):
|
||||
del argv
|
||||
|
||||
logging = minitaur_logging.MinitaurLogging()
|
||||
episode = logging.restore_episode(FLAGS.proto_file)
|
||||
#print(dir (episode))
|
||||
#print("episode=",episode)
|
||||
fields = episode.ListFields()
|
||||
|
||||
recs = []
|
||||
|
||||
for rec in fields[0][1]:
|
||||
#print(rec.time)
|
||||
for motorState in rec.motor_states:
|
||||
#print("motorState.angle=",motorState.angle)
|
||||
#print("motorState.velocity=",motorState.velocity)
|
||||
#print("motorState.action=",motorState.action)
|
||||
#print("motorState.torque=",motorState.torque)
|
||||
recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque])
|
||||
|
||||
a = numpy.array(recs)
|
||||
numpy.savetxt(FLAGS.csv_file, a, delimiter=",")
|
||||
|
||||
if __name__ == "__main__":
|
||||
tf.app.run(main)
|
||||
|
||||
|
||||
@ -0,0 +1,6 @@
|
||||
pybullet
|
||||
tensorflow
|
||||
gym
|
||||
pandas
|
||||
matplotlib
|
||||
|
||||
@ -172,7 +172,7 @@ def save_config(config, logdir=None):
|
||||
tf.logging.info(message.format(config.logdir))
|
||||
tf.gfile.MakeDirs(config.logdir)
|
||||
config_path = os.path.join(config.logdir, 'config.yaml')
|
||||
with tf.gfile.FastGFile(config_path, 'w') as file_:
|
||||
with tf.gfile.GFile(config_path, 'w') as file_:
|
||||
yaml.dump(config, file_, default_flow_style=False)
|
||||
else:
|
||||
message = (
|
||||
|
||||
@ -249,7 +249,8 @@ class MinitaurGymEnv(gym.Env):
|
||||
self._hard_reset = hard_reset # This assignment need to be after reset()
|
||||
|
||||
def close(self):
|
||||
self.logging.save_episode(self._episode_proto)
|
||||
if self._env_step_counter>0:
|
||||
self.logging.save_episode(self._episode_proto)
|
||||
self.minitaur.Terminate()
|
||||
|
||||
def add_env_randomizer(self, env_randomizer):
|
||||
@ -258,7 +259,8 @@ class MinitaurGymEnv(gym.Env):
|
||||
def reset(self, initial_motor_angles=None, reset_duration=1.0):
|
||||
self._pybullet_client.configureDebugVisualizer(
|
||||
self._pybullet_client.COV_ENABLE_RENDERING, 0)
|
||||
self.logging.save_episode(self._episode_proto)
|
||||
if self._env_step_counter>0:
|
||||
self.logging.save_episode(self._episode_proto)
|
||||
self._episode_proto = minitaur_logging_pb2.MinitaurEpisode()
|
||||
minitaur_logging.preallocate_episode_proto(self._episode_proto,
|
||||
self._num_steps_to_log)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user