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Unversion manipulators for now. They come back as they are ready.

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This commit is contained in:
Benelot
2017-08-16 08:25:05 +02:00
parent 68106d66dd
commit f74a9299a8
3 changed files with 27 additions and 331 deletions
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54
examples/pybullet/gym/envs/__init__.py
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@@ -72,33 +72,33 @@ register(
reward_threshold=800.0, reward_threshold=800.0,
) )
register( # register(
id='ReacherBulletEnv-v0', # id='ReacherBulletEnv-v0',
entry_point='envs.gym_manipulator_envs:ReacherBulletEnv', # entry_point='envs.gym_manipulator_envs:ReacherBulletEnv',
max_episode_steps=150, # max_episode_steps=150,
reward_threshold=18.0, # reward_threshold=18.0,
) # )
#
register( # register(
id='PusherBulletEnv-v0', # id='PusherBulletEnv-v0',
entry_point='envs.gym_manipulator_envs:PusherBulletEnv', # entry_point='envs.gym_manipulator_envs:PusherBulletEnv',
max_episode_steps=150, # max_episode_steps=150,
reward_threshold=18.0, # reward_threshold=18.0,
) # )
#
register( # register(
id='ThrowerBulletEnv-v0', # id='ThrowerBulletEnv-v0',
entry_point='envs.gym_manipulator_envs:ThrowerBulletEnv', # entry_point='envs.gym_manipulator_envs:ThrowerBulletEnv',
max_episode_steps=100, # max_episode_steps=100,
reward_threshold=18.0, # reward_threshold=18.0,
) # )
#
register( # register(
id='StrikerBulletEnv-v0', # id='StrikerBulletEnv-v0',
entry_point='envs.gym_manipulator_envs:StrikerBulletEnv', # entry_point='envs.gym_manipulator_envs:StrikerBulletEnv',
max_episode_steps=100, # max_episode_steps=100,
reward_threshold=18.0, # reward_threshold=18.0,
) # )
register( register(
id='Walker2DBulletEnv-v0', id='Walker2DBulletEnv-v0',

304
examples/pybullet/gym/envs/gym_manipulator_envs.py
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@@ -1,304 +0,0 @@
from scene_abstract import SingleRobotEmptyScene
from env_bases import MujocoXmlBaseBulletEnv
import gym, gym.spaces, gym.utils, gym.utils.seeding
import numpy as np
import os, sys
class ReacherBulletEnv(MujocoXmlBaseBulletEnv):
def __init__(self):
PybulletMujocoXmlEnv.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9)
def create_single_player_scene(self):
return SingleRobotEmptyScene(gravity=0.0, timestep=0.0165, frame_skip=1)
TARG_LIMIT = 0.27
def robot_specific_reset(self):
self.jdict["target_x"].reset_current_position(self.np_random.uniform( low=-self.TARG_LIMIT, high=self.TARG_LIMIT ), 0)
self.jdict["target_y"].reset_current_position(self.np_random.uniform( low=-self.TARG_LIMIT, high=self.TARG_LIMIT ), 0)
self.fingertip = self.parts["fingertip"]
self.target = self.parts["target"]
self.central_joint = self.jdict["joint0"]
self.elbow_joint = self.jdict["joint1"]
self.central_joint.reset_current_position(self.np_random.uniform( low=-3.14, high=3.14 ), 0)
self.elbow_joint.reset_current_position(self.np_random.uniform( low=-3.14, high=3.14 ), 0)
def apply_action(self, a):
assert( np.isfinite(a).all() )
self.central_joint.set_motor_torque( 0.05*float(np.clip(a[0], -1, +1)) )
self.elbow_joint.set_motor_torque( 0.05*float(np.clip(a[1], -1, +1)) )
def calc_state(self):
theta, self.theta_dot = self.central_joint.current_relative_position()
self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position()
target_x, _ = self.jdict["target_x"].current_position()
target_y, _ = self.jdict["target_y"].current_position()
self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz())
return np.array([
target_x,
target_y,
self.to_target_vec[0],
self.to_target_vec[1],
np.cos(theta),
np.sin(theta),
self.theta_dot,
self.gamma,
self.gamma_dot,
])
def calc_potential(self):
return -100 * np.linalg.norm(self.to_target_vec)
def _step(self, a):
assert(not self.scene.multiplayer)
self.apply_action(a)
self.scene.global_step()
state = self.calc_state() # sets self.to_target_vec
potential_old = self.potential
self.potential = self.calc_potential()
electricity_cost = (
-0.10*(np.abs(a[0]*self.theta_dot) + np.abs(a[1]*self.gamma_dot)) # work torque*angular_velocity
-0.01*(np.abs(a[0]) + np.abs(a[1])) # stall torque require some energy
)
stuck_joint_cost = -0.1 if np.abs(np.abs(self.gamma)-1) < 0.01 else 0.0
self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)]
self.HUD(state, a, False)
return state, sum(self.rewards), False, {}
def camera_adjust(self):
x, y, z = self.fingertip.pose().xyz()
x *= 0.5
y *= 0.5
self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
class PusherBulletEnv(MujocoXmlBaseBulletEnv):
def __init__(self):
PybulletMujocoXmlEnv.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=5)
def create_single_player_scene(self):
return SingleRobotEmptyScene(gravity=9.81, timestep=0.0020, frame_skip=5)
def robot_specific_reset(self):
self.fingertip = self.parts["fingertip"]
# qpos = self.init_qpos
self.goal_pos = np.asarray([0, 0])
while True:
self.cylinder_pos = np.concatenate([
self.np_random.uniform(low=-0.3, high=0, size=1),
self.np_random.uniform(low=-0.2, high=0.2, size=1)])
if np.linalg.norm(self.cylinder_pos - self.goal_pos) > 0.17:
break
# This is probably position setting
# qpos[-4:-2] = self.cylinder_pos
# qpos[-2:] = self.goal_pos
# qvel = self.init_qvel + self.np_random.uniform(low=-0.005,
# high=0.005, size=self.model.nv)
# qvel[-4:] = 0
# self.set_state(qpos, qvel)
def apply_action(self, a):
assert( np.isfinite(a).all() )
def calc_state(self):
return np.concatenate([
np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position
np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed
self.parts["fingertip"].pose().xyz(),
self.parts["object"].pose().xyz(),
self.parts["goal"].pose().xyz(),
])
def _step(self, a):
self.apply_action(a)
self.scene.global_step()
state = self.calc_state()
reward_near_vec = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz()
reward_dist_vec = self.parts["object"].pose().xyz() - self.parts["goal"].pose().xyz()
reward_near = - np.linalg.norm(reward_near_vec)
reward_dist = - np.linalg.norm(reward_dist_vec)
reward_ctrl = - np.square(a).sum()
reward = reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near
done = False
return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def camera_adjust(self):
x, y, z = self.fingertip.pose().xyz()
x *= 0.5
y *= 0.5
self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
class StrikerBulletEnv(MujocoXmlBaseBulletEnv):
def __init__(self):
PybulletMujocoXmlEnv.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=5)
self._striked = False
self._min_strike_dist = np.inf
self.strike_threshold = 0.1
def create_single_player_scene(self):
return SingleRobotEmptyScene(gravity=9.81, timestep=0.0020, frame_skip=5)
def robot_specific_reset(self):
self.fingertip = self.parts["fingertip"]
self._min_strike_dist = np.inf
self._striked = False
self._strike_pos = None
# reset position of manipulator
for j in self.ordered_joints:
j.reset_current_position(self.np_random.uniform( low=-0.1, high=0.1 ))
# reset speed of manipulator
# reset ball position
# qpos = self.init_qpos
self.ball = np.array([0.5, -0.175])
while True:
self.goal = np.concatenate([
self.np_random.uniform(low=0.15, high=0.7, size=1),
self.np_random.uniform(low=0.1, high=1.0, size=1)])
if np.linalg.norm(self.ball - self.goal) > 0.17:
break
# This is probably position setting
# qpos[-9:-7] = [self.ball[1], self.ball[0]]
# qpos[-7:-5] = self.goal
# diff = self.ball - self.goal
# angle = -np.arctan(diff[0] / (diff[1] + 1e-8))
# qpos[-1] = angle / 3.14
# qvel = self.init_qvel + self.np_random.uniform(low=-.1, high=.1,
# size=self.model.nv)
# qvel[7:] = 0
# self.set_state(qpos, qvel)
def apply_action(self, a):
assert( np.isfinite(a).all() )
def calc_state(self):
return np.concatenate([
np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position
np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed
self.parts["fingertip"].pose().xyz(),
self.parts["object"].pose().xyz(),
self.parts["goal"].pose().xyz(),
])
def _step(self, a):
self.apply_action(a)
self.scene.global_step()
state = self.calc_state()
dist_object_finger = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz()
reward_dist_vec = self.parts["object"].pose().xyz() - self.parts["goal"].pose().xyz()
self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec))
if np.linalg.norm(dist_object_finger) < self.strike_threshold:
self._striked = True
self._strike_pos = self.parts["fingertip"].pose().xyz()
if self._striked:
reward_near_vec = self.parts["object"].pose().xyz() - self._strike_pos
else:
reward_near_vec = self.parts["object"].pose().xyz() - self.parts["fingertip"].pose().xyz()
reward_near = - np.linalg.norm(reward_near_vec)
reward_dist = - np.linalg.norm(self._min_strike_dist)
reward_ctrl = - np.square(a).sum()
reward = 3 * reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near
done = False
return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def camera_adjust(self):
x, y, z = self.fingertip.pose().xyz()
x *= 0.5
y *= 0.5
self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
class ThrowerBulletEnv(MujocoXmlBaseBulletEnv):
def __init__(self):
PybulletMujocoXmlEnv.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=5)
self._ball_hit_ground = False
self._ball_hit_location = None
def create_single_player_scene(self):
return SingleRobotEmptyScene(gravity=0.0, timestep=0.0020, frame_skip=5)
def robot_specific_reset(self):
self.fingertip = self.parts["fingertip"]
self._ball_hit_ground = False
self._ball_hit_location = None
# reset position of manipulator
for j in self.ordered_joints:
j.reset_current_position(self.np_random.uniform( low=-0.1, high=0.1 ))
# reset speed of manipulator
# This is probably position setting
# qpos = self.init_qpos
# self.goal = np.array([self.np_random.uniform(low=-0.3, high=0.3),
# self.np_random.uniform(low=-0.3, high=0.3)])
#
# qpos[-9:-7] = self.goal
# qvel = self.init_qvel + self.np_random.uniform(low=-0.005,
# high=0.005, size=self.model.nv)
# qvel[7:] = 0
# self.set_state(qpos, qvel)
def apply_action(self, a):
assert( np.isfinite(a).all() )
def calc_state(self):
return np.concatenate([
np.array([j.current_position() for j in self.ordered_joints]).flatten(), # position
np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # speed
self.parts["fingertip"].pose().xyz(),
self.parts["ball"].pose().xyz(),
self.parts["goal"].pose().xyz(),
])
def _step(self, a):
self.apply_action(a)
self.scene.global_step()
state = self.calc_state()
ball_xy = self.parts["ball"].pose().xyz()[:2]
goal_xy = self.parts["goal"].pose().xyz()[:2]
if not self._ball_hit_ground and self.parts["ball"].pose().xyz()[2] < -0.25:
self._ball_hit_ground = True
self._ball_hit_location = self.parts["ball"].pose().xyz()
if self._ball_hit_ground:
ball_hit_xy = self._ball_hit_location[:2]
reward_dist = -np.linalg.norm(ball_hit_xy - goal_xy)
else:
reward_dist = -np.linalg.norm(ball_xy - goal_xy)
reward_ctrl = - np.square(a).sum()
reward = reward_dist + 0.002 * reward_ctrl
done = False
return state, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def camera_adjust(self):
x, y, z = self.fingertip.pose().xyz()
x *= 0.5
y *= 0.5
self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)

0
examples/pybullet/gym/envs/robot_manipulators.py
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