mirror of
https://github.com/bulletphysics/bullet3
synced 2024-12-15 06:00:12 +00:00
5fdd7ed3b4
Reshaped image array should be of size (RENDER_HEIGHT, RENDER_WIDTH, 4) instead of (RENDER_WIDTH, RENDER_HEIGHT, 4).
291 lines
9.5 KiB
Python
291 lines
9.5 KiB
Python
import os, inspect
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currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
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print ("current_dir=" + currentdir)
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os.sys.path.insert(0,currentdir)
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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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import pybullet_data
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from pkg_resources import parse_version
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largeValObservation = 100
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RENDER_HEIGHT = 720
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RENDER_WIDTH = 960
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class KukaGymEnv(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=pybullet_data.getDataPath(),
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actionRepeat=1,
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isEnableSelfCollision=True,
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renders=False,
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isDiscrete=False,
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maxSteps = 1000):
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#print("KukaGymEnv __init__")
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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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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._maxSteps = maxSteps
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self.terminated = 0
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self._cam_dist = 1.3
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self._cam_yaw = 180
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self._cam_pitch = -40
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self._p = p
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if self._renders:
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cid = p.connect(p.SHARED_MEMORY)
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if (cid<0):
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cid = 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([largeValObservation] * 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("KukaGymEnv _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(self._urdfRoot,"plane.urdf"),[0,0,-1])
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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 = 0.55 +0.12*random.random()
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ypos = 0 +0.2*random.random()
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ang = 3.14*0.5+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,-0.15,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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self._observation = self._kuka.getObservation()
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gripperState = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaGripperIndex)
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gripperPos = gripperState[0]
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gripperOrn = gripperState[1]
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blockPos,blockOrn = p.getBasePositionAndOrientation(self.blockUid)
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invGripperPos,invGripperOrn = p.invertTransform(gripperPos,gripperOrn)
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gripperMat = p.getMatrixFromQuaternion(gripperOrn)
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dir0 = [gripperMat[0],gripperMat[3],gripperMat[6]]
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dir1 = [gripperMat[1],gripperMat[4],gripperMat[7]]
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dir2 = [gripperMat[2],gripperMat[5],gripperMat[8]]
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gripperEul = p.getEulerFromQuaternion(gripperOrn)
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#print("gripperEul")
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#print(gripperEul)
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blockPosInGripper,blockOrnInGripper = p.multiplyTransforms(invGripperPos,invGripperOrn,blockPos,blockOrn)
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projectedBlockPos2D =[blockPosInGripper[0],blockPosInGripper[1]]
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blockEulerInGripper = p.getEulerFromQuaternion(blockOrnInGripper)
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#print("projectedBlockPos2D")
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#print(projectedBlockPos2D)
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#print("blockEulerInGripper")
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#print(blockEulerInGripper)
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#we return the relative x,y position and euler angle of block in gripper space
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blockInGripperPosXYEulZ =[blockPosInGripper[0],blockPosInGripper[1],blockEulerInGripper[2]]
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#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1)
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#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1)
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#p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1)
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self._observation.extend(list(blockInGripperPosXYEulZ))
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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 = 0.005
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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,-0.05,0.05][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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#print("action[0]=", str(action[0]))
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dv = 0.005
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dx = action[0] * dv
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dy = action[1] * dv
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da = action[2] * 0.05
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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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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._termination():
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break
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self._envStepCounter += 1
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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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#print("self._envStepCounter")
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#print(self._envStepCounter)
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done = self._termination()
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npaction = np.array([action[3]]) #only penalize rotation until learning works well [action[0],action[1],action[3]])
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actionCost = np.linalg.norm(npaction)*10.
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#print("actionCost")
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#print(actionCost)
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reward = self._reward()-actionCost
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#print("reward")
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#print(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="rgb_array", close=False):
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if mode != "rgb_array":
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return np.array([])
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base_pos,orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid)
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view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
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cameraTargetPosition=base_pos,
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distance=self._cam_dist,
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yaw=self._cam_yaw,
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pitch=self._cam_pitch,
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roll=0,
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upAxisIndex=2)
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proj_matrix = self._p.computeProjectionMatrixFOV(
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fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
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nearVal=0.1, farVal=100.0)
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(_, _, px, _, _) = self._p.getCameraImage(
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width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
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projectionMatrix=proj_matrix, renderer=self._p.ER_BULLET_HARDWARE_OPENGL)
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#renderer=self._p.ER_TINY_RENDERER)
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rgb_array = np.array(px, dtype=np.uint8)
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rgb_array = np.reshape(rgb_array, (RENDER_HEIGHT, RENDER_WIDTH, 4))
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rgb_array = rgb_array[:, :, :3]
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return rgb_array
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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>self._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 (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
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self.terminated = 1
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#print("terminating, 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 (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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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, -1, self._kuka.kukaEndEffectorIndex)
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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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reward = reward+10000
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print("successfully grasped a block!!!")
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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#print("self._envStepCounter")
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#print(self._envStepCounter)
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#print("reward")
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#print(reward)
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#print("reward")
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#print(reward)
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return reward
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if parse_version(gym.__version__)>=parse_version('0.9.6'):
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render = _render
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reset = _reset
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seed = _seed
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step = _step
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