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fetch_reach.py
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fetch_reach.py
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from gym import utils
import rospy
from gym import spaces
from openai_ros.robot_envs import fetch_env_v2
from gym.envs.registration import register
import numpy as np
from sensor_msgs.msg import JointState
from fetch_train.srv import EePose, EePoseRequest, EeRpy, EeRpyRequest, EeTraj, EeTrajRequest, JointTraj, JointTrajRequest
register(
id='FetchReach-v0',
entry_point='openai_ros:task_envs.fetch_reach.fetch_reach.FetchReachEnv',
timestep_limit=1000,
)
class FetchReachEnv(fetch_env_v2.FetchEnv, utils.EzPickle):
def __init__(self):
print ("Entered Reach Env")
self.get_params()
fetch_env_v2.FetchEnv.__init__(self)
utils.EzPickle.__init__(self)
print ("Call env setup")
self._env_setup(initial_qpos=self.init_pos)
print ("Call get_obs")
obs = self._get_obs()
self.action_space = spaces.Box(-1., 1., shape=(self.n_actions,), dtype='float32')
self.observation_space = spaces.Dict(dict(
desired_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'),
achieved_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'),
observation=spaces.Box(-np.inf, np.inf, shape=obs['observation'].shape, dtype='float32'),
))
def get_params(self):
#get configuration parameters
"""
self.n_actions = rospy.get_param('/fetch/n_actions')
self.has_object = rospy.get_param('/fetch/has_object')
self.block_gripper = rospy.get_param('/fetch/block_gripper')
self.n_substeps = rospy.get_param('/fetch/n_substeps')
self.gripper_extra_height = rospy.get_param('/fetch/gripper_extra_height')
self.target_in_the_air = rospy.get_param('/fetch/target_in_the_air')
self.target_offset = rospy.get_param('/fetch/target_offset')
self.obj_range = rospy.get_param('/fetch/obj_range')
self.target_range = rospy.get_param('/fetch/target_range')
self.distance_threshold = rospy.get_param('/fetch/distance_threshold')
self.init_pos = rospy.get_param('/fetch/init_pos')
self.reward_type = rospy.get_param('/fetch/reward_type')
"""
self.n_actions = 4
self.has_object = False
self.block_gripper = True
self.n_substeps = 20
self.gripper_extra_height = 0.2
self.target_in_the_air = True
self.target_offset = 0.0
self.obj_range = 0.15
self.target_range = 0.15
self.distance_threshold = 0.05
self.reward_type = "sparse"
self.init_pos = {
'joint0': 0.0,
'joint1': 0.0,
'joint2': 0.0,
'joint3': -1.5,
'joint4': 0.0,
'joint5': 1.5,
'joint6': 0.0,
}
def _set_action(self, action):
# Take action
assert action.shape == (4,)
action = action.copy() # ensure that we don't change the action outside of this scope
pos_ctrl, gripper_ctrl = action[:3], action[3]
#pos_ctrl *= 0.05 # limit maximum change in position
rot_ctrl = [1., 0., 1., 0.] # fixed rotation of the end effector, expressed as a quaternion
gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])
assert gripper_ctrl.shape == (2,)
if self.block_gripper:
gripper_ctrl = np.zeros_like(gripper_ctrl)
action = np.concatenate([pos_ctrl, rot_ctrl, gripper_ctrl])
# Apply action to simulation.
self.set_trajectory_ee(action)
def _get_obs(self):
grip_pos = self.get_ee_pose()
grip_pos_array = np.array([grip_pos.pose.position.x, grip_pos.pose.position.y, grip_pos.pose.position.z])
#dt = self.sim.nsubsteps * self.sim.model.opt.timestep #What is this??
#grip_velp = self.sim.data.get_site_xvelp('robot0:grip') * dt
grip_rpy = self.get_ee_rpy()
#print grip_rpy
grip_velp = np.array([grip_rpy.y, grip_rpy.y])
robot_qpos, robot_qvel = self.robot_get_obs(self.joints)
if self.has_object:
object_pos = self.sim.data.get_site_xpos('object0')
# rotations
object_rot = rotations.mat2euler(self.sim.data.get_site_xmat('object0'))
# velocities
object_velp = self.sim.data.get_site_xvelp('object0') * dt
object_velr = self.sim.data.get_site_xvelr('object0') * dt
# gripper state
object_rel_pos = object_pos - grip_pos
object_velp -= grip_velp
else:
object_pos = object_rot = object_velp = object_velr = object_rel_pos = np.zeros(0)
gripper_state = robot_qpos[-2:]
gripper_vel = robot_qvel[-2:] #* dt # change to a scalar if the gripper is made symmetric
"""
if not self.has_object:
achieved_goal = grip_pos_array.copy()
else:
achieved_goal = np.squeeze(object_pos.copy())
"""
achieved_goal = self._sample_achieved_goal(grip_pos_array, object_pos)
obs = np.concatenate([
grip_pos_array, object_pos.ravel(), object_rel_pos.ravel(), gripper_state, object_rot.ravel(),
object_velp.ravel(), object_velr.ravel(), gripper_vel,
])
return {
'observation': obs.copy(),
'achieved_goal': achieved_goal.copy(),
'desired_goal': self.goal.copy(),
}
def _is_done(self, observations):
d = self.goal_distance(observations['achieved_goal'], self.goal)
return (d < self.distance_threshold).astype(np.float32)
def _compute_reward(self, observations, done):
d = self.goal_distance(observations['achieved_goal'], self.goal)
if self.reward_type == 'sparse':
return -(d > self.distance_threshold).astype(np.float32)
else:
return -d
def _init_env_variables(self):
"""
Inits variables needed to be initialized each time we reset at the start
of an episode.
:return:
"""
pass
def _set_init_pose(self):
"""Sets the Robot in its init pose
"""
self.gazebo.unpauseSim()
self.set_trajectory_joints(self.init_pos)
return True
def goal_distance(self, goal_a, goal_b):
assert goal_a.shape == goal_b.shape
return np.linalg.norm(goal_a - goal_b, axis=-1)
def _sample_goal(self):
if self.has_object:
goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-self.target_range, self.target_range, size=3)
goal += self.target_offset
goal[2] = self.height_offset
if self.target_in_the_air and self.np_random.uniform() < 0.5:
goal[2] += self.np_random.uniform(0, 0.45)
else:
goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-0.15, 0.15, size=3)
#return goal.copy()
return goal
def _sample_achieved_goal(self, grip_pos_array, object_pos):
if not self.has_object:
achieved_goal = grip_pos_array.copy()
else:
achieved_goal = np.squeeze(object_pos.copy())
#return achieved_goal.copy()
return achieved_goal
def _env_setup(self, initial_qpos):
print ("Init Pos:")
print (initial_qpos)
#for name, value in initial_qpos.items():
self.gazebo.unpauseSim()
self.set_trajectory_joints(initial_qpos)
#self.execute_trajectory()
#utils.reset_mocap_welds(self.sim)
#self.sim.forward()
# Move end effector into position.
gripper_target = np.array([0.498, 0.005, 0.431 + self.gripper_extra_height])# + self.sim.data.get_site_xpos('robot0:grip')
gripper_rotation = np.array([1., 0., 1., 0.])
#self.sim.data.set_mocap_pos('robot0:mocap', gripper_target)
#self.sim.data.set_mocap_quat('robot0:mocap', gripper_rotation)
action = np.concatenate([gripper_target, gripper_rotation])
self.set_trajectory_ee(action)
#self.execute_trajectory()
#for _ in range(10):
#self.sim.step()
#self.step()
# Extract information for sampling goals.
#self.initial_gripper_xpos = self.sim.data.get_site_xpos('robot0:grip').copy()
gripper_pos = self.get_ee_pose()
gripper_pose_array = np.array([gripper_pos.pose.position.x, gripper_pos.pose.position.y, gripper_pos.pose.position.z])
self.initial_gripper_xpos = gripper_pose_array.copy()
if self.has_object:
self.height_offset = self.sim.data.get_site_xpos('object0')[2]
self.goal = self._sample_goal()
self._get_obs()
def robot_get_obs(self, data):
"""
Returns all joint positions and velocities associated with a robot.
"""
if data.position is not None and data.name:
#names = [n for n in data.name if n.startswith('robot')]
names = [n for n in data.name]
i = 0
r = 0
for name in names:
r += 1
return (
np.array([data.position[i] for i in range(r)]),
np.array([data.velocity[i] for i in range(r)]),
)
return np.zeros(0), np.zeros(0)