148 lines
4.3 KiB
Python
148 lines
4.3 KiB
Python
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 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="",
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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._p = p
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if self._renders:
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p.connect(p.GUI)
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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(-observation_high, observation_high)
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self.viewer = None
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def _reset(self):
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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("%splane.urdf" % self._urdfRoot,[0,0,-1])
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if self._renders:
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p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
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p.loadURDF("table/table.urdf", 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
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xpos = 0.5 +0.25*random.random()
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ypos = 0 +0.22*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("block.urdf", xpos,ypos,0.02,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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pos,orn = p.getBasePositionAndOrientation(self.blockUid)
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self._observation.extend(list(pos))
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self._observation.extend(list(orn))
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return self._observation
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def _step(self, action):
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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,-dv,dv][action]
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f = 0.3
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realAction = [dx,dy,-0.1,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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if (self._envStepCounter>1000):
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for i in range (1000):
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p.stepSimulation()
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#start grasp and terminate
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fingerAngle = 0.3
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for i in range (5000):
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graspAction = [0,0,0,0,fingerAngle]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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fingerAngle = fingerAngle-(0.3/5000.)
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for i in range (5000):
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graspAction = [0,0,0.0001,0,0]
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self._kuka.applyAction(graspAction)
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p.stepSimulation()
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fingerAngle = fingerAngle-(0.3/10000.)
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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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pos,orn=p.getBasePositionAndOrientation(self.blockUid)
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reward = pos[2]
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if (reward>0.2):
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print("reward")
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print(reward)
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return reward |