refactor pybullet/gym to allow instantiating environments directly from a pybullet install:
work-in-progress (need to add missing data files, fix paths etc)
example:
pip install pybullet
pip install gym
python
import gym
import pybullet
import pybullet_envs
env = gym.make("HumanoidBulletEnv-v0")
This commit is contained in:
Erwin Coumans
192
examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
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192
examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
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import os
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import math
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import gym
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from gym import spaces
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from gym.utils import seeding
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import numpy as np
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import time
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import pybullet as p
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from . import kuka
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import random
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class KukaCamGymEnv(gym.Env):
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metadata = {
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'render.modes': ['human', 'rgb_array'],
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'video.frames_per_second' : 50
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}
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def __init__(self,
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urdfRoot="",
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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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self._timeStep = 1./240.
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self._urdfRoot = urdfRoot
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self._actionRepeat = actionRepeat
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self._isEnableSelfCollision = isEnableSelfCollision
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self._observation = []
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self._envStepCounter = 0
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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.terminated = 0
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self._p = p
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if self._renders:
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p.connect(p.GUI)
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p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
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else:
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p.connect(p.DIRECT)
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#timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json")
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self._seed()
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self.reset()
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observationDim = len(self.getExtendedObservation())
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#print("observationDim")
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#print(observationDim)
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observation_high = np.array([np.finfo(np.float32).max] * observationDim)
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self.action_space = spaces.Discrete(7)
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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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p.setTimeStep(self._timeStep)
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p.loadURDF(os.path.join(os.path.dirname(__file__),"../data","plane.urdf"),[0,0,-1])
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p.loadURDF(os.path.join(os.path.dirname(__file__),"../data","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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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(os.path.dirname(__file__),"../data","block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
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p.setGravity(0,0,-10)
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self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
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self._envStepCounter = 0
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p.stepSimulation()
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self._observation = self.getExtendedObservation()
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return np.array(self._observation)
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def __del__(self):
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p.disconnect()
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def _seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def getExtendedObservation(self):
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#camEyePos = [0.03,0.236,0.54]
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#distance = 1.06
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#pitch=-56
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#yaw = 258
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#roll=0
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#upAxisIndex = 2
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#camInfo = p.getDebugVisualizerCamera()
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#print("width,height")
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#print(camInfo[0])
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#print(camInfo[1])
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#print("viewMatrix")
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#print(camInfo[2])
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#print("projectionMatrix")
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#print(camInfo[3])
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#viewMat = camInfo[2]
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#viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex)
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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]
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#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]
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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]
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img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
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rgb=img_arr[2]
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np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
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self._observation = np_img_arr
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return self._observation
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def _step(self, action):
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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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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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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._envStepCounter += 1
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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done = self._termination()
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reward = self._reward()
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#print("len=%r" % len(self._observation))
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return np.array(self._observation), reward, done, {}
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def _render(self, mode='human', close=False):
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return
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def _termination(self):
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#print (self._kuka.endEffectorPos[2])
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state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
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actualEndEffectorPos = state[0]
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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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self._observation = self.getExtendedObservation()
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return True
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if (actualEndEffectorPos[2] <= 0.10):
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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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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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self._observation = self.getExtendedObservation()
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return True
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return False
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def _reward(self):
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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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reward = -1000
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numPt = len(closestPoints)
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#print(numPt)
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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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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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