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