Bart/bullet3
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge remote-tracking branch 'upstream/master'

Browse Source
This commit is contained in:
yunfeibai
2018-02-15 14:31:34 -08:00
parent cf4a0a3503
commit 36dcabbaf5
50 changed files with 332 additions and 138 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py
View File

@@ -47,7 +47,7 @@ def main():
print(obs)
episode_rew = 0
while not done:
env.render()
env.render(mode='human')
act = policy.sample_action(obs, .1)
print("Action")
print(act)

12
examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py
View File

@@ -17,7 +17,7 @@ import time
import subprocess
import pybullet as p
import pybullet_data
from pkg_resources import parse_version
logger = logging.getLogger(__name__)
@@ -64,10 +64,10 @@ class CartPoleBulletEnv(gym.Env):
# time.sleep(self.timeStep)
self.state = p.getJointState(self.cartpole, 1)[0:2] + p.getJointState(self.cartpole, 0)[0:2]
theta, theta_dot, x, x_dot = self.state
dv = 0.1
deltav = [-10.*dv,-5.*dv, -2.*dv, -0.1*dv, 0, 0.1*dv, 2.*dv,5.*dv, 10.*dv][action]
p.setJointMotorControl2(self.cartpole, 0, p.VELOCITY_CONTROL, targetVelocity=(deltav + self.state[3]))
done = x < -self.x_threshold \
@@ -99,3 +99,9 @@ class CartPoleBulletEnv(gym.Env):
def _render(self, mode='human', close=False):
return
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

49
examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
View File

@@ -14,6 +14,8 @@ import pybullet as p
from . import kuka
import random
import pybullet_data
from pkg_resources import parse_version
maxSteps = 1000
RENDER_HEIGHT = 720
@@ -56,8 +58,8 @@ class KukaCamGymEnv(gym.Env):
observationDim = len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
if (self._isDiscrete):
self.action_space = spaces.Discrete(7)
else:
@@ -74,15 +76,15 @@ class KukaCamGymEnv(gym.Env):
p.setPhysicsEngineParameter(numSolverIterations=150)
p.setTimeStep(self._timeStep)
p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1])
p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
xpos = 0.5 +0.2*random.random()
ypos = 0 +0.25*random.random()
ang = 3.1415925438*random.random()
orn = p.getQuaternionFromEuler([0,0,ang])
self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
p.setGravity(0,0,-10)
self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
self._envStepCounter = 0
@@ -98,7 +100,7 @@ class KukaCamGymEnv(gym.Env):
return [seed]
def getExtendedObservation(self):
#camEyePos = [0.03,0.236,0.54]
#distance = 1.06
#pitch=-56
@@ -118,13 +120,13 @@ class KukaCamGymEnv(gym.Env):
viewMat = [-0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722, -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843, 0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0]
#projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
projMatrix = [0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
rgb=img_arr[2]
np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
self._observation = np_img_arr
return self._observation
def _step(self, action):
if (self._isDiscrete):
dv = 0.01
@@ -142,7 +144,7 @@ class KukaCamGymEnv(gym.Env):
realAction = [dx,dy,-0.002,da,f]
return self.step2( realAction)
def step2(self, action):
for i in range(self._actionRepeat):
self._kuka.applyAction(action)
@@ -158,11 +160,11 @@ class KukaCamGymEnv(gym.Env):
#print("self._envStepCounter")
#print(self._envStepCounter)
done = self._termination()
reward = self._reward()
#print("len=%r" % len(self._observation))
return np.array(self._observation), reward, done, {}
def _render(self, mode='human', close=False):
@@ -190,18 +192,18 @@ class KukaCamGymEnv(gym.Env):
#print (self._kuka.endEffectorPos[2])
state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
actualEndEffectorPos = state[0]
#print("self._envStepCounter")
#print(self._envStepCounter)
if (self.terminated or self._envStepCounter>maxSteps):
self._observation = self.getExtendedObservation()
return True
maxDist = 0.005
maxDist = 0.005
closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
self.terminated = 1
#print("closing gripper, attempting grasp")
#start grasp and terminate
fingerAngle = 0.3
@@ -212,7 +214,7 @@ class KukaCamGymEnv(gym.Env):
fingerAngle = fingerAngle-(0.3/100.)
if (fingerAngle<0):
fingerAngle=0
for i in range (1000):
graspAction = [0,0,0.001,0,fingerAngle]
self._kuka.applyAction(graspAction)
@@ -227,18 +229,18 @@ class KukaCamGymEnv(gym.Env):
if (actualEndEffectorPos[2]>0.5):
break
self._observation = self.getExtendedObservation()
return True
return False
def _reward(self):
#rewards is height of target object
blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
reward = -1000
reward = -1000
numPt = len(closestPoints)
#print(numPt)
if (numPt>0):
@@ -254,3 +256,8 @@ class KukaCamGymEnv(gym.Env):
#print(reward)
return reward
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

71
examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py
View File

@@ -13,6 +13,7 @@ import pybullet as p
from . import kuka
import random
import pybullet_data
from pkg_resources import parse_version
largeValObservation = 100
@@ -44,7 +45,7 @@ class KukaGymEnv(gym.Env):
self._maxSteps = maxSteps
self.terminated = 0
self._cam_dist = 1.3
self._cam_yaw = 180
self._cam_yaw = 180
self._cam_pitch = -40
self._p = p
@@ -61,8 +62,8 @@ class KukaGymEnv(gym.Env):
observationDim = len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
observation_high = np.array([largeValObservation] * observationDim)
observation_high = np.array([largeValObservation] * observationDim)
if (self._isDiscrete):
self.action_space = spaces.Discrete(7)
else:
@@ -80,15 +81,15 @@ class KukaGymEnv(gym.Env):
p.setPhysicsEngineParameter(numSolverIterations=150)
p.setTimeStep(self._timeStep)
p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1])
p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
xpos = 0.55 +0.12*random.random()
ypos = 0 +0.2*random.random()
ang = 3.14*0.5+3.1415925438*random.random()
orn = p.getQuaternionFromEuler([0,0,ang])
self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.15,orn[0],orn[1],orn[2],orn[3])
p.setGravity(0,0,-10)
self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
self._envStepCounter = 0
@@ -115,7 +116,7 @@ class KukaGymEnv(gym.Env):
dir0 = [gripperMat[0],gripperMat[3],gripperMat[6]]
dir1 = [gripperMat[1],gripperMat[4],gripperMat[7]]
dir2 = [gripperMat[2],gripperMat[5],gripperMat[8]]
gripperEul = p.getEulerFromQuaternion(gripperOrn)
#print("gripperEul")
#print(gripperEul)
@@ -126,17 +127,17 @@ class KukaGymEnv(gym.Env):
#print(projectedBlockPos2D)
#print("blockEulerInGripper")
#print(blockEulerInGripper)
#we return the relative x,y position and euler angle of block in gripper space
blockInGripperPosXYEulZ =[blockPosInGripper[0],blockPosInGripper[1],blockEulerInGripper[2]]
#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1)
#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1)
#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1)
self._observation.extend(list(blockInGripperPosXYEulZ))
return self._observation
def _step(self, action):
if (self._isDiscrete):
dv = 0.005
@@ -154,7 +155,7 @@ class KukaGymEnv(gym.Env):
f = 0.3
realAction = [dx,dy,-0.002,da,f]
return self.step2( realAction)
def step2(self, action):
for i in range(self._actionRepeat):
self._kuka.applyAction(action)
@@ -168,7 +169,7 @@ class KukaGymEnv(gym.Env):
#print("self._envStepCounter")
#print(self._envStepCounter)
done = self._termination()
npaction = np.array([action[3]]) #only penalize rotation until learning works well [action[0],action[1],action[3]])
actionCost = np.linalg.norm(npaction)*10.
@@ -177,14 +178,15 @@ class KukaGymEnv(gym.Env):
reward = self._reward()-actionCost
#print("reward")
#print(reward)
#print("len=%r" % len(self._observation))
return np.array(self._observation), reward, done, {}
def _render(self, mode="rgb_array", close=False):
if mode != "rgb_array":
return np.array([])
base_pos,orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid)
view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
cameraTargetPosition=base_pos,
@@ -199,7 +201,12 @@ class KukaGymEnv(gym.Env):
(_, _, px, _, _) = self._p.getCameraImage(
width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
projectionMatrix=proj_matrix, renderer=self._p.ER_BULLET_HARDWARE_OPENGL)
rgb_array = np.array(px)
#renderer=self._p.ER_TINY_RENDERER)
rgb_array = np.array(px, dtype=np.uint8)
rgb_array = np.reshape(rgb_array, (RENDER_WIDTH, RENDER_HEIGHT, 4))
rgb_array = rgb_array[:, :, :3]
return rgb_array
@@ -208,18 +215,18 @@ class KukaGymEnv(gym.Env):
#print (self._kuka.endEffectorPos[2])
state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
actualEndEffectorPos = state[0]
#print("self._envStepCounter")
#print(self._envStepCounter)
if (self.terminated or self._envStepCounter>self._maxSteps):
self._observation = self.getExtendedObservation()
return True
maxDist = 0.005
maxDist = 0.005
closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
self.terminated = 1
#print("terminating, closing gripper, attempting grasp")
#start grasp and terminate
fingerAngle = 0.3
@@ -230,7 +237,7 @@ class KukaGymEnv(gym.Env):
fingerAngle = fingerAngle-(0.3/100.)
if (fingerAngle<0):
fingerAngle=0
for i in range (1000):
graspAction = [0,0,0.001,0,fingerAngle]
self._kuka.applyAction(graspAction)
@@ -245,19 +252,19 @@ class KukaGymEnv(gym.Env):
if (actualEndEffectorPos[2]>0.5):
break
self._observation = self.getExtendedObservation()
return True
return False
def _reward(self):
#rewards is height of target object
blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
reward = -1000
numPt = len(closestPoints)
#print(numPt)
if (numPt>0):
@@ -276,10 +283,8 @@ class KukaGymEnv(gym.Env):
#print(reward)
return reward
def reset(self):
"""Resets the state of the environment and returns an initial observation.
Returns: observation (object): the initial observation of the
space.
"""
return self._reset()
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

11
examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py
View File

@@ -10,7 +10,8 @@ import pybullet_data
import pdb
import distutils.dir_util
import glob
from pkg_resources import parse_version
import gym
class KukaDiverseObjectEnv(KukaGymEnv):
"""Class for Kuka environment with diverse objects.
@@ -323,4 +324,10 @@ class KukaDiverseObjectEnv(KukaGymEnv):
selected_objects_filenames = []
for object_index in selected_objects:
selected_objects_filenames += [found_object_directories[object_index]]
return selected_objects_filenames
return selected_objects_filenames
if parse_version(gym.__version__)>=parse_version('0.9.6'):
reset = _reset
step = _step

5
examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py
View File

@@ -385,3 +385,8 @@ class MinitaurBulletDuckEnv(gym.Env):
scale=self._observation_noise_stdev, size=observation.shape) *
self.minitaur.GetObservationUpperBound())
return observation
render = _render
reset = _reset
seed = _seed
step = _step

7
examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py
View File

@@ -20,6 +20,7 @@ from . import minitaur
import os
import pybullet_data
from . import minitaur_env_randomizer
from pkg_resources import parse_version
NUM_SUBSTEPS = 5
NUM_MOTORS = 8
@@ -376,3 +377,9 @@ class MinitaurBulletEnv(gym.Env):
scale=self._observation_noise_stdev, size=observation.shape) *
self.minitaur.GetObservationUpperBound())
return observation
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

39
examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py
View File

@@ -14,6 +14,7 @@ from . import racecar
import random
from . import bullet_client
import pybullet_data
from pkg_resources import parse_version
RENDER_HEIGHT = 720
RENDER_WIDTH = 960
@@ -50,8 +51,8 @@ class RacecarGymEnv(gym.Env):
#self.reset()
observationDim = 2 #len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
# observation_high = np.array([np.finfo(np.float32).max] * observationDim)
#print(observationDim)
# observation_high = np.array([np.finfo(np.float32).max] * observationDim)
observation_high = np.ones(observationDim) * 1000 #np.inf
if (isDiscrete):
self.action_space = spaces.Discrete(9)
@@ -59,7 +60,7 @@ class RacecarGymEnv(gym.Env):
action_dim = 2
self._action_bound = 1
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(-observation_high, observation_high)
self.viewer = None
@@ -74,14 +75,14 @@ class RacecarGymEnv(gym.Env):
# pos,orn = self._p.getBasePositionAndOrientation(i)
# newpos = [pos[0],pos[1],pos[2]-0.1]
# self._p.resetBasePositionAndOrientation(i,newpos,orn)
dist = 5 +2.*random.random()
ang = 2.*3.1415925438*random.random()
ballx = dist * math.sin(ang)
bally = dist * math.cos(ang)
ballz = 1
self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2.urdf"),[ballx,bally,ballz])
self._p.setGravity(0,0,-10)
self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
@@ -101,18 +102,18 @@ class RacecarGymEnv(gym.Env):
def getExtendedObservation(self):
self._observation = [] #self._racecar.getObservation()
carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn)
ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
self._observation.extend([ballPosInCar[0],ballPosInCar[1]])
return self._observation
def _step(self, action):
if (self._renders):
basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
#self._p.resetDebugVisualizerCamera(1, 30, -40, basePos)
if (self._isDiscrete):
fwd = [-1,-1,-1,0,0,0,1,1,1]
steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6]
@@ -128,14 +129,14 @@ class RacecarGymEnv(gym.Env):
if self._renders:
time.sleep(self._timeStep)
self._observation = self.getExtendedObservation()
if self._termination():
break
self._envStepCounter += 1
reward = self._reward()
done = self._termination()
#print("len=%r" % len(self._observation))
return np.array(self._observation), reward, done, {}
def _render(self, mode='human', close=False):
@@ -159,13 +160,13 @@ class RacecarGymEnv(gym.Env):
rgb_array = rgb_array[:, :, :3]
return rgb_array
def _termination(self):
return self._envStepCounter>1000
def _reward(self):
closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
numPt = len(closestPoints)
reward=-1000
#print(numPt)
@@ -174,3 +175,9 @@ class RacecarGymEnv(gym.Env):
reward = -closestPoints[0][8]
#print(reward)
return reward
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

37
examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py
View File

@@ -14,6 +14,7 @@ from . import bullet_client
from . import racecar
import random
import pybullet_data
from pkg_resources import parse_version
RENDER_HEIGHT = 720
RENDER_WIDTH = 960
@@ -40,7 +41,7 @@ class RacecarZEDGymEnv(gym.Env):
self._renders = renders
self._width = 100
self._height = 10
self._isDiscrete = isDiscrete
if self._renders:
self._p = bullet_client.BulletClient(
@@ -53,8 +54,8 @@ class RacecarZEDGymEnv(gym.Env):
observationDim = len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
if (isDiscrete):
self.action_space = spaces.Discrete(9)
else:
@@ -77,14 +78,14 @@ class RacecarZEDGymEnv(gym.Env):
pos,orn = self._p.getBasePositionAndOrientation(i)
newpos = [pos[0],pos[1],pos[2]+0.1]
self._p.resetBasePositionAndOrientation(i,newpos,orn)
dist = 5 +2.*random.random()
ang = 2.*3.1415925438*random.random()
ballx = dist * math.sin(ang)
bally = dist * math.cos(ang)
ballz = 1
self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2red.urdf"),[ballx,bally,ballz])
self._p.setGravity(0,0,-10)
self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
@@ -104,13 +105,13 @@ class RacecarZEDGymEnv(gym.Env):
def getExtendedObservation(self):
carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
carmat = self._p.getMatrixFromQuaternion(carorn)
ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn)
ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
dist0 = 0.3
dist1 = 1.
eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3]
targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
up = [carmat[2],carmat[5],carmat[8]]
viewMat = self._p.computeViewMatrix(eyePos,targetPos,up)
#viewMat = self._p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
@@ -122,12 +123,12 @@ class RacecarZEDGymEnv(gym.Env):
np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
self._observation = np_img_arr
return self._observation
def _step(self, action):
if (self._renders):
basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
#self._p.resetDebugVisualizerCamera(1, 30, -40, basePos)
if (self._isDiscrete):
fwd = [-1,-1,-1,0,0,0,1,1,1]
steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6]
@@ -143,14 +144,14 @@ class RacecarZEDGymEnv(gym.Env):
if self._renders:
time.sleep(self._timeStep)
self._observation = self.getExtendedObservation()
if self._termination():
break
self._envStepCounter += 1
reward = self._reward()
done = self._termination()
#print("len=%r" % len(self._observation))
return np.array(self._observation), reward, done, {}
def _render(self, mode='human', close=False):
@@ -177,10 +178,10 @@ class RacecarZEDGymEnv(gym.Env):
def _termination(self):
return self._envStepCounter>1000
def _reward(self):
closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
numPt = len(closestPoints)
reward=-1000
#print(numPt)
@@ -189,3 +190,9 @@ class RacecarZEDGymEnv(gym.Env):
reward = -closestPoints[0][8]
#print(reward)
return reward
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

24
examples/pybullet/gym/pybullet_envs/bullet/simpleHumanoidGymEnv.py
View File

@@ -12,7 +12,7 @@ import time
import pybullet as p
from . import simpleHumanoid
import random
from pkg_resources import parse_version
import pybullet_data
@@ -46,8 +46,8 @@ class SimpleHumanoidGymEnv(gym.Env):
observationDim = len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
self.action_space = spaces.Discrete(9)
self.observation_space = spaces.Box(-observation_high, observation_high)
self.viewer = None
@@ -57,14 +57,14 @@ class SimpleHumanoidGymEnv(gym.Env):
#p.setPhysicsEngineParameter(numSolverIterations=300)
p.setTimeStep(self._timeStep)
p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"))
dist = 5 +2.*random.random()
ang = 2.*3.1415925438*random.random()
ballx = dist * math.sin(ang)
bally = dist * math.cos(ang)
ballz = 1
p.setGravity(0,0,-10)
self._humanoid = simpleHumanoid.SimpleHumanoid(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
self._envStepCounter = 0
@@ -82,7 +82,7 @@ class SimpleHumanoidGymEnv(gym.Env):
def getExtendedObservation(self):
self._observation = self._humanoid.getObservation()
return self._observation
def _step(self, action):
self._humanoid.applyAction(action)
for i in range(self._actionRepeat):
@@ -96,7 +96,7 @@ class SimpleHumanoidGymEnv(gym.Env):
reward = self._reward()
done = self._termination()
#print("len=%r" % len(self._observation))
return np.array(self._observation), reward, done, {}
def _render(self, mode='human', close=False):
@@ -104,8 +104,14 @@ class SimpleHumanoidGymEnv(gym.Env):
def _termination(self):
return self._envStepCounter>1000
def _reward(self):
reward=self._humanoid.distance
print(reward)
return reward
if parse_version(gym.__version__)>=parse_version('0.9.6'):
render = _render
reset = _reset
seed = _seed
step = _step

21
examples/pybullet/gym/pybullet_envs/env_bases.py
View File

@@ -1,7 +1,7 @@
import gym, gym.spaces, gym.utils, gym.utils.seeding
import numpy as np
import pybullet as p
from pkg_resources import parse_version
class MJCFBaseBulletEnv(gym.Env):
"""
@@ -43,7 +43,7 @@ class MJCFBaseBulletEnv(gym.Env):
conInfo = p.getConnectionInfo()
if (conInfo['isConnected']):
self.ownsPhysicsClient = False
self.physicsClientId = 0
else:
self.ownsPhysicsClient = True
@@ -75,12 +75,12 @@ class MJCFBaseBulletEnv(gym.Env):
self.isRender = True
if mode != "rgb_array":
return np.array([])
base_pos=[0,0,0]
if (hasattr(self,'robot')):
if (hasattr(self.robot,'body_xyz')):
base_pos = self.robot.body_xyz
view_matrix = p.computeViewMatrixFromYawPitchRoll(
cameraTargetPosition=base_pos,
distance=self._cam_dist,
@@ -100,6 +100,7 @@ class MJCFBaseBulletEnv(gym.Env):
rgb_array = rgb_array[:, :, :3]
return rgb_array
def _close(self):
if (self.ownsPhysicsClient):
if (self.physicsClientId>=0):
@@ -109,6 +110,18 @@ class MJCFBaseBulletEnv(gym.Env):
def HUD(self, state, a, done):
pass
# backwards compatibility for gym >= v0.9.x
# for extension of this class.
def step(self, *args, **kwargs):
return self._step(*args, **kwargs)
if parse_version(gym.__version__)>=parse_version('0.9.6'):
close = _close
render = _render
reset = _reset
seed = _seed
class Camera:
def __init__(self):
pass

6
examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py
View File

@@ -1,6 +1,12 @@
r"""An example to run of the minitaur gym environment with sine gaits.
"""
import os
import 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)
import math
import numpy as np
from pybullet_envs.bullet import minitaur_gym_env

6
examples/pybullet/gym/pybullet_envs/examples/testBike.py
View File

@@ -1,3 +1,9 @@
import os
import 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)
import pybullet as p
import math
import time