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import os, 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 gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import pybullet as p
from . import kuka
import random
import pybullet_data
from pkg_resources import parse_version
maxSteps = 1000
RENDER_HEIGHT = 720
RENDER_WIDTH = 960
class KukaCamGymEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
def __init__(self,
urdfRoot=pybullet_data.getDataPath(),
actionRepeat=1,
isEnableSelfCollision=True,
renders=False,
isDiscrete=False):
self._timeStep = 1. / 240.
self._urdfRoot = urdfRoot
self._actionRepeat = actionRepeat
self._isEnableSelfCollision = isEnableSelfCollision
self._observation = []
self._envStepCounter = 0
self._renders = renders
self._width = 341
self._height = 256
self._isDiscrete = isDiscrete
self.terminated = 0
self._p = p
if self._renders:
cid = p.connect(p.SHARED_MEMORY)
if (cid < 0):
p.connect(p.GUI)
p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33])
else:
p.connect(p.DIRECT)
#timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json")
self.seed()
self.reset()
observationDim = len(self.getExtendedObservation())
#print("observationDim")
#print(observationDim)
observation_high = np.array([np.finfo(np.float32).max] * observationDim)
if (self._isDiscrete):
self.action_space = spaces.Discrete(7)
else:
action_dim = 3
self._action_bound = 1
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self._height, self._width, 4),
dtype=np.uint8)
self.viewer = None
def reset(self):
self.terminated = 0
p.resetSimulation()
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
p.stepSimulation()
self._observation = self.getExtendedObservation()
return np.array(self._observation)
def __del__(self):
p.disconnect()
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def getExtendedObservation(self):
#camEyePos = [0.03,0.236,0.54]
#distance = 1.06
#pitch=-56
#yaw = 258
#roll=0
#upAxisIndex = 2
#camInfo = p.getDebugVisualizerCamera()
#print("width,height")
#print(camInfo[0])
#print(camInfo[1])
#print("viewMatrix")
#print(camInfo[2])
#print("projectionMatrix")
#print(camInfo[3])
#viewMat = camInfo[2]
#viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex)
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
dx = [0, -dv, dv, 0, 0, 0, 0][action]
dy = [0, 0, 0, -dv, dv, 0, 0][action]
da = [0, 0, 0, 0, 0, -0.1, 0.1][action]
f = 0.3
realAction = [dx, dy, -0.002, da, f]
else:
dv = 0.01
dx = action[0] * dv
dy = action[1] * dv
da = action[2] * 0.1
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)
p.stepSimulation()
if self._termination():
break
#self._observation = self.getExtendedObservation()
self._envStepCounter += 1
self._observation = self.getExtendedObservation()
if self._renders:
time.sleep(self._timeStep)
#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):
if mode != "rgb_array":
return np.array([])
base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
distance=self._cam_dist,
yaw=self._cam_yaw,
pitch=self._cam_pitch,
roll=0,
upAxisIndex=2)
proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
nearVal=0.1,
farVal=100.0)
(_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
height=RENDER_HEIGHT,
viewMatrix=view_matrix,
projectionMatrix=proj_matrix,
renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
rgb_array = np.array(px)
rgb_array = rgb_array[:, :, :3]
return rgb_array
def _termination(self):
#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
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
for i in range(100):
graspAction = [0, 0, 0.0001, 0, fingerAngle]
self._kuka.applyAction(graspAction)
p.stepSimulation()
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)
p.stepSimulation()
blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
if (blockPos[2] > 0.23):
#print("BLOCKPOS!")
#print(blockPos[2])
break
state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
actualEndEffectorPos = state[0]
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)
reward = -1000
numPt = len(closestPoints)
#print(numPt)
if (numPt > 0):
#print("reward:")
reward = -closestPoints[0][8] * 10
if (blockPos[2] > 0.2):
#print("grasped a block!!!")
#print("self._envStepCounter")
#print(self._envStepCounter)
reward = reward + 1000
#print("reward")
#print(reward)
return reward
if parse_version(gym.__version__) < parse_version('0.9.6'):
_render = render
_reset = reset
_seed = seed
_step = step
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