mirror of
https://github.com/bulletphysics/bullet3
synced 2024-12-15 06:00:12 +00:00
add continuous versions of kukaGymEnv, kukaCamGymEnv, racecarZEDGymEnv etc.
should be trainable with PPO or evolution strategies (ES) now
This commit is contained in:
Erwin Coumans
parent
32312e60a8
commit
55f5e52ecd
@ -17,12 +17,12 @@ class Kuka:
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self.timeStep = timeStep
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self.maxForce = 200.
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self.fingerAForce = 6
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self.fingerBForce = 5.5
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self.fingerTipForce = 6
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self.fingerAForce = 2
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self.fingerBForce = 2.5
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self.fingerTipForce = 2
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self.useInverseKinematics = 1
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self.useSimulation = 1
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self.useNullSpace = 1
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self.useNullSpace =21
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self.useOrientation = 1
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self.kukaEndEffectorIndex = 6
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#lower limits for null space
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@ -120,10 +120,8 @@ class Kuka:
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#print (self.endEffectorPos[2])
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#print("actualEndEffectorPos[2]")
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#print(actualEndEffectorPos[2])
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if (dz>0 or actualEndEffectorPos[2]>0.10):
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#if (dz<0 or actualEndEffectorPos[2]<0.5):
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self.endEffectorPos[2] = self.endEffectorPos[2]+dz
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if (actualEndEffectorPos[2]<0.10):
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self.endEffectorPos[2] = self.endEffectorPos[2]+0.0001
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self.endEffectorAngle = self.endEffectorAngle + da
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@ -147,7 +145,7 @@ class Kuka:
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if (self.useSimulation):
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for i in range (self.kukaEndEffectorIndex+1):
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#print(i)
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p.setJointMotorControl2(bodyIndex=self.kukaUid,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=self.maxForce,positionGain=0.03,velocityGain=1)
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p.setJointMotorControl2(bodyIndex=self.kukaUid,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=self.maxForce,positionGain=0.3,velocityGain=1)
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else:
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#reset the joint state (ignoring all dynamics, not recommended to use during simulation)
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for i in range (self.numJoints):
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@ -14,7 +14,7 @@ import pybullet as p
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from . import kuka
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import random
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import pybullet_data
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maxSteps = 1000
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class KukaCamGymEnv(gym.Env):
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metadata = {
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@ -24,10 +24,10 @@ class KukaCamGymEnv(gym.Env):
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def __init__(self,
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urdfRoot=pybullet_data.getDataPath(),
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actionRepeat=1,
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actionRepeat=25,
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isEnableSelfCollision=True,
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renders=True):
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print("init")
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renders=True,
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isDiscrete=False):
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self._timeStep = 1./240.
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self._urdfRoot = urdfRoot
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self._actionRepeat = actionRepeat
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@ -37,6 +37,7 @@ class KukaCamGymEnv(gym.Env):
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self._renders = renders
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self._width = 341
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self._height = 256
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self._isDiscrete=isDiscrete
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self.terminated = 0
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self._p = p
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if self._renders:
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@ -54,12 +55,17 @@ class KukaCamGymEnv(gym.Env):
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#print(observationDim)
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observation_high = np.array([np.finfo(np.float32).max] * observationDim)
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if (self._isDiscrete):
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self.action_space = spaces.Discrete(7)
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else:
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action_dim = 3
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self._action_bound = 1
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action_high = np.array([self._action_bound] * action_dim)
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self.action_space = spaces.Box(-action_high, action_high)
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self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4))
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self.viewer = None
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def _reset(self):
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print("reset")
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self.terminated = 0
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p.resetSimulation()
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p.setPhysicsEngineParameter(numSolverIterations=150)
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@ -68,8 +74,8 @@ class KukaCamGymEnv(gym.Env):
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p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
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xpos = 0.5 +0.05*random.random()
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ypos = 0 +0.05*random.random()
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xpos = 0.5 +0.2*random.random()
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ypos = 0 +0.25*random.random()
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ang = 3.1415925438*random.random()
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orn = p.getQuaternionFromEuler([0,0,ang])
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self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
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@ -117,24 +123,36 @@ class KukaCamGymEnv(gym.Env):
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return self._observation
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def _step(self, action):
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if (self._isDiscrete):
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dv = 0.01
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dx = [0,-dv,dv,0,0,0,0][action]
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dy = [0,0,0,-dv,dv,0,0][action]
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da = [0,0,0,0,0,-0.1,0.1][action]
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f = 0.3
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realAction = [dx,dy,-0.002,da,f]
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else:
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dv = 0.01
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dx = action[0] * dv
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dy = action[1] * dv
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da = action[2] * 0.1
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f = 0.3
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realAction = [dx,dy,-0.002,da,f]
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return self.step2( realAction)
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def step2(self, action):
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self._kuka.applyAction(action)
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for i in range(self._actionRepeat):
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self._kuka.applyAction(action)
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p.stepSimulation()
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if self._renders:
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time.sleep(self._timeStep)
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self._observation = self.getExtendedObservation()
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if self._termination():
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break
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#self._observation = self.getExtendedObservation()
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self._envStepCounter += 1
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self._observation = self.getExtendedObservation()
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if self._renders:
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time.sleep(self._timeStep)
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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@ -154,25 +172,41 @@ class KukaCamGymEnv(gym.Env):
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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if (self.terminated or self._envStepCounter>1000):
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if (self.terminated or self._envStepCounter>maxSteps):
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self._observation = self.getExtendedObservation()
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return True
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maxDist = 0.005
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closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
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if (actualEndEffectorPos[2] <= 0.10):
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if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
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self.terminated = 1
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#print("closing gripper, attempting grasp")
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#start grasp and terminate
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fingerAngle = 0.3
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for i in range (1000):
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graspAction = [0,0,0.001,0,fingerAngle]
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for i in range (100):
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graspAction = [0,0,0.0001,0,fingerAngle]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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fingerAngle = fingerAngle-(0.3/100.)
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if (fingerAngle<0):
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fingerAngle=0
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for i in range (1000):
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graspAction = [0,0,0.001,0,fingerAngle]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
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if (blockPos[2] > 0.23):
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#print("BLOCKPOS!")
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#print(blockPos[2])
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break
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state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
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actualEndEffectorPos = state[0]
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if (actualEndEffectorPos[2]>0.5):
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break
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self._observation = self.getExtendedObservation()
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return True
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return False
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@ -181,7 +215,7 @@ class KukaCamGymEnv(gym.Env):
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#rewards is height of target object
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blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
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closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000)
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closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
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reward = -1000
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numPt = len(closestPoints)
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@ -189,13 +223,13 @@ class KukaCamGymEnv(gym.Env):
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if (numPt>0):
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#print("reward:")
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reward = -closestPoints[0][8]*10
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if (blockPos[2] >0.2):
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print("grasped a block!!!")
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print("self._envStepCounter")
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print(self._envStepCounter)
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#print("grasped a block!!!")
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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reward = reward+1000
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#print("reward")
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#print(reward)
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return reward
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@ -14,6 +14,8 @@ from . import kuka
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import random
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import pybullet_data
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maxSteps = 1000
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class KukaGymEnv(gym.Env):
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metadata = {
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@ -25,8 +27,9 @@ class KukaGymEnv(gym.Env):
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urdfRoot=pybullet_data.getDataPath(),
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actionRepeat=1,
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isEnableSelfCollision=True,
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renders=True):
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print("init")
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renders=True,
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isDiscrete=False):
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self._isDiscrete = isDiscrete
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self._timeStep = 1./240.
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self._urdfRoot = urdfRoot
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self._actionRepeat = actionRepeat
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@ -51,12 +54,17 @@ class KukaGymEnv(gym.Env):
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#print(observationDim)
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observation_high = np.array([np.finfo(np.float32).max] * observationDim)
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if (self._isDiscrete):
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self.action_space = spaces.Discrete(7)
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else:
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action_dim = 3
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self._action_bound = 1
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action_high = np.array([self._action_bound] * action_dim)
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self.action_space = spaces.Box(-action_high, action_high)
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self.observation_space = spaces.Box(-observation_high, observation_high)
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self.viewer = None
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def _reset(self):
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print("reset")
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self.terminated = 0
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p.resetSimulation()
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p.setPhysicsEngineParameter(numSolverIterations=150)
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@ -65,8 +73,8 @@ class KukaGymEnv(gym.Env):
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p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
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xpos = 0.5 +0.05*random.random()
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ypos = 0 +0.05*random.random()
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xpos = 0.5 +0.2*random.random()
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ypos = 0 +0.25*random.random()
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ang = 3.1415925438*random.random()
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orn = p.getQuaternionFromEuler([0,0,ang])
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self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
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@ -101,12 +109,20 @@ class KukaGymEnv(gym.Env):
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return self._observation
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def _step(self, action):
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if (self._isDiscrete):
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dv = 0.01
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dx = [0,-dv,dv,0,0,0,0][action]
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dy = [0,0,0,-dv,dv,0,0][action]
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da = [0,0,0,0,0,-0.1,0.1][action]
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f = 0.3
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realAction = [dx,dy,-0.002,da,f]
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else:
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dv = 0.01
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dx = action[0] * dv
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dy = action[1] * dv
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da = action[2] * 0.1
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f = 0.3
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realAction = [dx,dy,-0.002,da,f]
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return self.step2( realAction)
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def step2(self, action):
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@ -138,25 +154,41 @@ class KukaGymEnv(gym.Env):
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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if (self.terminated or self._envStepCounter>1000):
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if (self.terminated or self._envStepCounter>maxSteps):
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self._observation = self.getExtendedObservation()
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return True
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maxDist = 0.005
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closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
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if (actualEndEffectorPos[2] <= 0.10):
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if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
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self.terminated = 1
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#print("closing gripper, attempting grasp")
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#start grasp and terminate
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fingerAngle = 0.3
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for i in range (1000):
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graspAction = [0,0,0.001,0,fingerAngle]
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for i in range (100):
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graspAction = [0,0,0.0001,0,fingerAngle]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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fingerAngle = fingerAngle-(0.3/100.)
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if (fingerAngle<0):
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fingerAngle=0
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for i in range (1000):
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graspAction = [0,0,0.001,0,fingerAngle]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
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if (blockPos[2] > 0.23):
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#print("BLOCKPOS!")
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#print(blockPos[2])
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break
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state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
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actualEndEffectorPos = state[0]
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if (actualEndEffectorPos[2]>0.5):
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break
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self._observation = self.getExtendedObservation()
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return True
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return False
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@ -165,7 +197,7 @@ class KukaGymEnv(gym.Env):
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#rewards is height of target object
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blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
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closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000)
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closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
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reward = -1000
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numPt = len(closestPoints)
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@ -173,11 +205,10 @@ class KukaGymEnv(gym.Env):
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if (numPt>0):
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#print("reward:")
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reward = -closestPoints[0][8]*10
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if (blockPos[2] >0.2):
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print("grasped a block!!!")
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print("self._envStepCounter")
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print(self._envStepCounter)
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#print("grasped a block!!!")
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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reward = reward+1000
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#print("reward")
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@ -32,6 +32,9 @@ OBSERVATION_EPS = 0.01
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RENDER_HEIGHT = 720
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RENDER_WIDTH = 960
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duckStartPos = [0,0,0.25]
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duckStartOrn = [0.5,0.5,0.5,0.5]
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class MinitaurBulletEnv(gym.Env):
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"""The gym environment for the minitaur.
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@ -133,6 +136,7 @@ class MinitaurBulletEnv(gym.Env):
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self._on_rack = on_rack
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self._cam_dist = 1.0
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self._cam_yaw = 0
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self._duckId = -1
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self._cam_pitch = -30
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self._hard_reset = True
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self._kd_for_pd_controllers = kd_for_pd_controllers
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@ -176,7 +180,8 @@ class MinitaurBulletEnv(gym.Env):
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self._pybullet_client.setPhysicsEngineParameter(
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numSolverIterations=int(self._num_bullet_solver_iterations))
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self._pybullet_client.setTimeStep(self._time_step)
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self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
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self._groundId = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
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self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root,duckStartPos,duckStartOrn)
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self._pybullet_client.setGravity(0, 0, -10)
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acc_motor = self._accurate_motor_model_enabled
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motor_protect = self._motor_overheat_protection
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@ -196,7 +201,7 @@ class MinitaurBulletEnv(gym.Env):
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kd_for_pd_controllers=self._kd_for_pd_controllers))
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else:
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self.minitaur.Reset(reload_urdf=False)
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self._pybullet_client.resetBasePositionAndOrientation(self._duckId,duckStartPos,duckStartOrn)
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if self._env_randomizer is not None:
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self._env_randomizer.randomize_env(self)
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@ -322,6 +327,10 @@ class MinitaurBulletEnv(gym.Env):
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"""
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return np.array(self._observation[BASE_ORIENTATION_OBSERVATION_INDEX:])
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def lost_duck(self):
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points = self._pybullet_client.getContactPoints(self._duckId, self._groundId);
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return len(points)>0
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def is_fallen(self):
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"""Decide whether the minitaur has fallen.
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@ -342,7 +351,7 @@ class MinitaurBulletEnv(gym.Env):
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def _termination(self):
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position = self.minitaur.GetBasePosition()
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distance = math.sqrt(position[0]**2 + position[1]**2)
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return self.is_fallen() or distance > self._distance_limit
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return self.lost_duck() or self.is_fallen() or distance > self._distance_limit
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def _reward(self):
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current_base_position = self.minitaur.GetBasePosition()
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@ -9,7 +9,7 @@ import time
|
||||
|
||||
def main():
|
||||
|
||||
environment = KukaGymEnv(renders=True)
|
||||
environment = KukaGymEnv(renders=True,isDiscrete=False)
|
||||
|
||||
|
||||
motorsIds=[]
|
||||
@ -19,10 +19,10 @@ def main():
|
||||
#motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
|
||||
#motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
|
||||
|
||||
dv = 0.001
|
||||
dv = 1
|
||||
motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0))
|
||||
motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0))
|
||||
motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,-dv))
|
||||
motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,0))
|
||||
motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0))
|
||||
motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
|
||||
|
||||
@ -33,7 +33,7 @@ def main():
|
||||
for motorId in motorsIds:
|
||||
action.append(environment._p.readUserDebugParameter(motorId))
|
||||
|
||||
state, reward, done, info = environment.step2(action)
|
||||
state, reward, done, info = environment.step(action)
|
||||
obs = environment.getExtendedObservation()
|
||||
|
||||
if __name__=="__main__":
|
||||
|
||||
@ -44,9 +44,9 @@ def MotorOverheatExample():
|
||||
motor_kd=0.00,
|
||||
on_rack=False)
|
||||
|
||||
action = [2.0] * 8
|
||||
action = [.0] * 8
|
||||
for i in range(8):
|
||||
action[i] = 2.0 - 0.5 * (-1 if i % 2 == 0 else 1) * (-1 if i < 4 else 1)
|
||||
action[i] = .0 - 0.1 * (-1 if i % 2 == 0 else 1) * (-1 if i < 4 else 1)
|
||||
|
||||
steps = 500
|
||||
actions_and_observations = []
|
||||
@ -112,9 +112,9 @@ def SinePolicyExample():
|
||||
on_rack=False)
|
||||
sum_reward = 0
|
||||
steps = 20000
|
||||
amplitude_1_bound = 0.5
|
||||
amplitude_2_bound = 0.5
|
||||
speed = 40
|
||||
amplitude_1_bound = 0.1
|
||||
amplitude_2_bound = 0.1
|
||||
speed = 1
|
||||
|
||||
for step_counter in range(steps):
|
||||
time_step = 0.01
|
||||
@ -124,9 +124,9 @@ def SinePolicyExample():
|
||||
amplitude2 = amplitude_2_bound
|
||||
steering_amplitude = 0
|
||||
if t < 10:
|
||||
steering_amplitude = 0.5
|
||||
steering_amplitude = 0.1
|
||||
elif t < 20:
|
||||
steering_amplitude = -0.5
|
||||
steering_amplitude = -0.1
|
||||
else:
|
||||
steering_amplitude = 0
|
||||
|
||||
|
||||
@ -3,12 +3,13 @@ import os, inspect
|
||||
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
|
||||
parentdir = os.path.dirname(os.path.dirname(currentdir))
|
||||
os.sys.path.insert(0,parentdir)
|
||||
isDiscrete = False
|
||||
|
||||
from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
|
||||
|
||||
def main():
|
||||
|
||||
environment = RacecarZEDGymEnv(renders=True, isDiscrete=True)
|
||||
environment = RacecarZEDGymEnv(renders=True, isDiscrete=isDiscrete)
|
||||
|
||||
targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity",-1,1,0)
|
||||
steeringSlider = environment._p.addUserDebugParameter("steering",-1,1,0)
|
||||
@ -16,6 +17,7 @@ def main():
|
||||
while (True):
|
||||
targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider)
|
||||
steeringAngle = environment._p.readUserDebugParameter(steeringSlider)
|
||||
if (isDiscrete):
|
||||
discreteAction = 0
|
||||
if (targetVelocity<-0.33):
|
||||
discreteAction=0
|
||||
@ -30,11 +32,13 @@ def main():
|
||||
else:
|
||||
discreteAction=discreteAction+1
|
||||
action=discreteAction
|
||||
else:
|
||||
action=[targetVelocity,steeringAngle]
|
||||
|
||||
state, reward, done, info = environment.step(action)
|
||||
obs = environment.getExtendedObservation()
|
||||
print("obs")
|
||||
print(obs)
|
||||
#print("obs")
|
||||
#print(obs)
|
||||
|
||||
if __name__=="__main__":
|
||||
main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user