294 lines
10 KiB
Python
294 lines
10 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 = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
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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,
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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,
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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,
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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([
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action[3]
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]) #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(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(fov=60,
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aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
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nearVal=0.1,
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farVal=100.0)
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(_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
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height=RENDER_HEIGHT,
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viewMatrix=view_matrix,
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projectionMatrix=proj_matrix,
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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,
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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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