336 lines
15 KiB
Python
336 lines
15 KiB
Python
import os
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from typing import Optional
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import mujoco_py.builder
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import MujocoEnv
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# XML Variables
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ROBOT_COLLISION_OBJ = ["wrist_palm_link_convex_geom",
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"wrist_pitch_link_convex_decomposition_p1_geom",
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"wrist_pitch_link_convex_decomposition_p2_geom",
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"wrist_pitch_link_convex_decomposition_p3_geom",
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"wrist_yaw_link_convex_decomposition_p1_geom",
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"wrist_yaw_link_convex_decomposition_p2_geom",
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"forearm_link_convex_decomposition_p1_geom",
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"forearm_link_convex_decomposition_p2_geom",
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"upper_arm_link_convex_decomposition_p1_geom",
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"upper_arm_link_convex_decomposition_p2_geom",
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"shoulder_link_convex_decomposition_p1_geom",
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"shoulder_link_convex_decomposition_p2_geom",
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"shoulder_link_convex_decomposition_p3_geom",
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"base_link_convex_geom", "table_contact_geom"]
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CUP_COLLISION_OBJ = ["cup_geom_table3", "cup_geom_table4", "cup_geom_table5", "cup_geom_table6",
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"cup_geom_table7", "cup_geom_table8", "cup_geom_table9", "cup_geom_table10",
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"cup_geom_table15", "cup_geom_table16", "cup_geom_table17", "cup_geom1_table8"]
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class BeerPongEnv(MujocoEnv, utils.EzPickle):
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def __init__(self, frame_skip=2):
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self._steps = 0
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# Small Context -> Easier. Todo: Should we do different versions?
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# self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
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# "beerpong_wo_cup" + ".xml")
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# self._cup_pos_min = np.array([-0.32, -2.2])
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# self._cup_pos_max = np.array([0.32, -1.2])
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self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
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"beerpong_wo_cup_big_table" + ".xml")
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self._cup_pos_min = np.array([-1.42, -4.05])
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self._cup_pos_max = np.array([1.42, -1.25])
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self._start_pos = np.array([0.0, 1.35, 0.0, 1.18, 0.0, -0.786, -1.59])
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self._start_vel = np.zeros(7)
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self.release_step = 100 # time step of ball release
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self.ep_length = 600 // frame_skip
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self.repeat_action = frame_skip
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# TODO: If accessing IDs is easier in the (new) official mujoco bindings, remove this
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self.model = None
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self.site_id = lambda x: self.model.site_name2id(x)
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self.body_id = lambda x: self.model.body_name2id(x)
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self.geom_id = lambda x: self.model.geom_name2id(x)
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# for reward calculation
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self.dists = []
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self.dists_final = []
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self.action_costs = []
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self.ball_ground_contact_first = False
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self.ball_table_contact = False
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self.ball_wall_contact = False
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self.ball_cup_contact = False
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self.ball_in_cup = False
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self.dist_ground_cup = -1 # distance floor to cup if first floor contact
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MujocoEnv.__init__(self, self.xml_path, frame_skip=1, mujoco_bindings="mujoco_py")
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utils.EzPickle.__init__(self)
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@property
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def start_pos(self):
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return self._start_pos
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@property
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def start_vel(self):
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return self._start_vel
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def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
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self.dists = []
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self.dists_final = []
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self.action_costs = []
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self.ball_ground_contact_first = False
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self.ball_table_contact = False
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self.ball_wall_contact = False
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self.ball_cup_contact = False
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self.ball_in_cup = False
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self.dist_ground_cup = -1 # distance floor to cup if first floor contact
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return super().reset()
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def reset_model(self):
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init_pos_all = self.init_qpos.copy()
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init_pos_robot = self.start_pos
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init_vel = np.zeros_like(init_pos_all)
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self._steps = 0
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start_pos = init_pos_all
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start_pos[0:7] = init_pos_robot
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# TODO: Ask Max why we need to set the state twice.
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self.set_state(start_pos, init_vel)
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start_pos[7::] = self.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
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self.set_state(start_pos, init_vel)
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xy = self.np_random.uniform(self._cup_pos_min, self._cup_pos_max)
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xyz = np.zeros(3)
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xyz[:2] = xy
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xyz[-1] = 0.840
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self.model.body_pos[self.body_id("cup_table")] = xyz
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return self._get_obs()
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def step(self, a):
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crash = False
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for _ in range(self.repeat_action):
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applied_action = a + self.data.qfrc_bias[:len(a)].copy() / self.model.actuator_gear[:, 0]
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try:
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self.do_simulation(applied_action, self.frame_skip)
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# self.reward_function.check_contacts(self.sim) # I assume this is not important?
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if self._steps < self.release_step:
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self.data.qpos[7::] = self.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
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self.data.qvel[7::] = self.data.site_xvelp[self.site_id("init_ball_pos"), :].copy()
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crash = False
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except mujoco_py.builder.MujocoException:
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crash = True
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ob = self._get_obs()
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if not crash:
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reward, reward_infos = self._get_reward(applied_action)
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is_collided = reward_infos['is_collided']
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done = is_collided or self._steps == self.ep_length - 1
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self._steps += 1
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else:
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reward = -30
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done = True
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reward_infos = {"success": False, "ball_pos": np.zeros(3), "ball_vel": np.zeros(3), "is_collided": False}
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infos = dict(
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reward=reward,
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action=a,
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q_pos=self.data.qpos[0:7].ravel().copy(),
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q_vel=self.data.qvel[0:7].ravel().copy(), sim_crash=crash,
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)
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infos.update(reward_infos)
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return ob, reward, done, infos
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def _get_obs(self):
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theta = self.data.qpos.flat[:7]
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theta_dot = self.data.qvel.flat[:7]
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ball_pos = self.data.get_body_xpos("ball").copy()
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cup_goal_diff_final = ball_pos - self.data.get_site_xpos("cup_goal_final_table").copy()
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cup_goal_diff_top = ball_pos - self.data.get_site_xpos("cup_goal_table").copy()
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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theta_dot,
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cup_goal_diff_final,
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cup_goal_diff_top,
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self.model.body_pos[self.body_id("cup_table")][:2].copy(),
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# [self._steps], # Use TimeAwareObservation Wrapper instead ....
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])
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@property
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def dt(self):
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return super(BeerPongEnv, self).dt * self.repeat_action
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def _get_reward(self, action):
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goal_pos = self.data.get_site_xpos("cup_goal_table")
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ball_pos = self.data.get_body_xpos("ball")
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ball_vel = self.data.get_body_xvelp("ball")
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goal_final_pos = self.data.get_site_xpos("cup_goal_final_table")
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self._check_contacts()
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self.dists.append(np.linalg.norm(goal_pos - ball_pos))
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self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos))
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self.dist_ground_cup = np.linalg.norm(ball_pos - goal_pos) \
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if self.ball_ground_contact_first and self.dist_ground_cup == -1 else self.dist_ground_cup
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action_cost = np.sum(np.square(action))
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self.action_costs.append(np.copy(action_cost))
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# # ##################### Reward function which does not force to bounce once on the table (quad dist) #########
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# Is this needed?
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# self._is_collided = self._check_collision_with_itself([self.geom_id(name) for name in CUP_COLLISION_OBJ])
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if self._steps == self.ep_length - 1: # or self._is_collided:
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min_dist = np.min(self.dists)
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final_dist = self.dists_final[-1]
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if self.ball_ground_contact_first:
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min_dist_coeff, final_dist_coeff, ground_contact_dist_coeff, rew_offset = 1, 0.5, 2, -4
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else:
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if not self.ball_in_cup:
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if not self.ball_table_contact and not self.ball_cup_contact and not self.ball_wall_contact:
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min_dist_coeff, final_dist_coeff, ground_contact_dist_coeff, rew_offset = 1, 0.5, 0, -4
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else:
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min_dist_coeff, final_dist_coeff, ground_contact_dist_coeff, rew_offset = 1, 0.5, 0, -2
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else:
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min_dist_coeff, final_dist_coeff, ground_contact_dist_coeff, rew_offset = 0, 1, 0, 0
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action_cost = 1e-4 * np.mean(action_cost)
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reward = rew_offset - min_dist_coeff * min_dist ** 2 - final_dist_coeff * final_dist ** 2 - \
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action_cost - ground_contact_dist_coeff * self.dist_ground_cup ** 2
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# release step punishment
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min_time_bound = 0.1
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max_time_bound = 1.0
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release_time = self.release_step * self.dt
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release_time_rew = int(release_time < min_time_bound) * (-30 - 10 * (release_time - min_time_bound) ** 2) + \
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int(release_time > max_time_bound) * (-30 - 10 * (release_time - max_time_bound) ** 2)
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reward += release_time_rew
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success = self.ball_in_cup
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else:
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action_cost = 1e-2 * action_cost
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reward = - action_cost
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success = False
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# ##############################################################################################################
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infos = {"success": success, "ball_pos": ball_pos.copy(),
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"ball_vel": ball_vel.copy(), "action_cost": action_cost, "task_reward": reward,
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"table_contact_first": int(not self.ball_ground_contact_first),
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"is_collided": False} # TODO: Check if is collided is needed
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return reward, infos
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def _check_contacts(self):
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if not self.ball_table_contact:
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self.ball_table_contact = self._check_collision({self.geom_id("ball_geom")},
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{self.geom_id("table_contact_geom")})
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if not self.ball_cup_contact:
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self.ball_cup_contact = self._check_collision({self.geom_id("ball_geom")},
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{self.geom_id(name) for name in CUP_COLLISION_OBJ})
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if not self.ball_wall_contact:
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self.ball_wall_contact = self._check_collision({self.geom_id("ball_geom")},
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{self.geom_id("wall")})
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if not self.ball_in_cup:
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self.ball_in_cup = self._check_collision({self.geom_id("ball_geom")},
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{self.geom_id("cup_base_table_contact")})
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if not self.ball_ground_contact_first:
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if not self.ball_table_contact and not self.ball_cup_contact and not self.ball_wall_contact \
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and not self.ball_in_cup:
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self.ball_ground_contact_first = self._check_collision({self.geom_id("ball_geom")},
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{self.geom_id("ground")})
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# Checks if id_set1 has a collision with id_set2
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def _check_collision(self, id_set_1, id_set_2):
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"""
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If id_set_2 is set to None, it will check for a collision with itself (id_set_1).
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"""
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collision_id_set = id_set_2 - id_set_1 if id_set_2 is not None else id_set_1
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for coni in range(self.data.ncon):
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con = self.data.contact[coni]
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if ((con.geom1 in id_set_1 and con.geom2 in collision_id_set) or
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(con.geom2 in id_set_1 and con.geom1 in collision_id_set)):
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return True
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return False
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class BeerPongEnvFixedReleaseStep(BeerPongEnv):
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def __init__(self, frame_skip=2):
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super().__init__(frame_skip)
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self.release_step = 62 # empirically evaluated for frame_skip=2!
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class BeerPongEnvStepBasedEpisodicReward(BeerPongEnv):
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def __init__(self, frame_skip=2):
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super().__init__(frame_skip)
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self.release_step = 62 # empirically evaluated for frame_skip=2!
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def step(self, a):
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if self._steps < self.release_step:
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return super(BeerPongEnvStepBasedEpisodicReward, self).step(a)
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else:
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reward = 0
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done = False
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while not done:
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sub_ob, sub_reward, done, sub_infos = super(BeerPongEnvStepBasedEpisodicReward, self).step(
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np.zeros(a.shape))
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reward += sub_reward
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infos = sub_infos
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ob = sub_ob
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ob[-1] = self.release_step + 1 # Since we simulate until the end of the episode, PPO does not see the
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# internal steps and thus, the observation also needs to be set correctly
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return ob, reward, done, infos
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# class ALRBeerBongEnvStepBased(ALRBeerBongEnv):
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# def __init__(self, frame_skip=1, apply_gravity_comp=True, noisy=False, rndm_goal=False, cup_goal_pos=None):
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# super().__init__(frame_skip, apply_gravity_comp, noisy, rndm_goal, cup_goal_pos)
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# self.release_step = 62 # empirically evaluated for frame_skip=2!
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#
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# def step(self, a):
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# if self._steps < self.release_step:
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# return super(ALRBeerBongEnvStepBased, self).step(a)
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# else:
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# reward = 0
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# done = False
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# while not done:
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# sub_ob, sub_reward, done, sub_infos = super(ALRBeerBongEnvStepBased, self).step(np.zeros(a.shape))
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# if not done or sub_infos['sim_crash']:
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# reward += sub_reward
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# else:
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# ball_pos = self.sim.data.body_xpos[self.sim.model._body_name2id["ball"]].copy()
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# cup_goal_dist_final = np.linalg.norm(ball_pos - self.sim.data.site_xpos[
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# self.sim.model._site_name2id["cup_goal_final_table"]].copy())
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# cup_goal_dist_top = np.linalg.norm(ball_pos - self.sim.data.site_xpos[
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# self.sim.model._site_name2id["cup_goal_table"]].copy())
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# if sub_infos['success']:
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# dist_rew = -cup_goal_dist_final ** 2
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# else:
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# dist_rew = -0.5 * cup_goal_dist_final ** 2 - cup_goal_dist_top ** 2
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# reward = reward - sub_infos['action_cost'] + dist_rew
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# infos = sub_infos
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# ob = sub_ob
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# ob[-1] = self.release_step + 1 # Since we simulate until the end of the episode, PPO does not see the
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# # internal steps and thus, the observation also needs to be set correctly
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# return ob, reward, done, infos
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if __name__ == "__main__":
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env = BeerPongEnv(frame_skip=2)
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env.seed(0)
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# env = ALRBeerBongEnvStepBased(frame_skip=2)
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# env = ALRBeerBongEnvStepBasedEpisodicReward(frame_skip=2)
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# env = ALRBeerBongEnvFixedReleaseStep(frame_skip=2)
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import time
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env.reset()
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env.render("human")
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for i in range(600):
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# ac = 10 * env.action_space.sample()
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ac = 0.05 * np.ones(7)
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obs, rew, d, info = env.step(ac)
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env.render("human")
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if d:
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print('reward:', rew)
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print('RESETTING')
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env.reset()
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time.sleep(1)
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env.close()
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