fancy_gym/alr_envs/alr/mujoco/beerpong/beerpong_reward.py
2021-08-25 17:16:20 +02:00

125 lines
4.9 KiB
Python

import numpy as np
from alr_envs.alr.mujoco import alr_reward_fct
class BeerpongReward(alr_reward_fct.AlrReward):
def __init__(self, sim, sim_time):
self.sim = sim
self.sim_time = sim_time
self.collision_objects = ["cup_geom1", "cup_geom2", "wrist_palm_link_convex_geom",
"wrist_pitch_link_convex_decomposition_p1_geom",
"wrist_pitch_link_convex_decomposition_p2_geom",
"wrist_pitch_link_convex_decomposition_p3_geom",
"wrist_yaw_link_convex_decomposition_p1_geom",
"wrist_yaw_link_convex_decomposition_p2_geom",
"forearm_link_convex_decomposition_p1_geom",
"forearm_link_convex_decomposition_p2_geom"]
self.ball_id = None
self.ball_collision_id = None
self.goal_id = None
self.goal_final_id = None
self.collision_ids = None
self.ball_traj = None
self.dists = None
self.dists_ctxt = None
self.dists_final = None
self.costs = None
self.reset(None)
def reset(self, context):
self.ball_traj = np.zeros(shape=(self.sim_time, 3))
self.dists = []
self.dists_ctxt = []
self.dists_final = []
self.costs = []
self.context = context
self.ball_in_cup = False
self.dist_ctxt = 5
self.ball_id = self.sim.model._body_name2id["ball"]
self.ball_collision_id = self.sim.model._geom_name2id["ball_geom"]
self.cup_robot_id = self.sim.model._site_name2id["cup_robot_final"]
self.goal_id = self.sim.model._site_name2id["cup_goal_table"]
self.goal_final_id = self.sim.model._site_name2id["cup_goal_final_table"]
self.collision_ids = [self.sim.model._geom_name2id[name] for name in self.collision_objects]
self.cup_table_id = self.sim.model._body_name2id["cup_table"]
def compute_reward(self, action, sim, step):
action_cost = np.sum(np.square(action))
stop_sim = False
success = False
if self.check_collision(sim):
reward = - 1e-4 * action_cost - 1000
stop_sim = True
return reward, success, stop_sim
# Compute the current distance from the ball to the inner part of the cup
goal_pos = sim.data.site_xpos[self.goal_id]
ball_pos = sim.data.body_xpos[self.ball_id]
goal_final_pos = sim.data.site_xpos[self.goal_final_id]
self.dists.append(np.linalg.norm(goal_pos - ball_pos))
self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos))
self.dists_ctxt.append(np.linalg.norm(ball_pos - self.context))
self.ball_traj[step, :] = ball_pos
# Determine the first time when ball is in cup
if not self.ball_in_cup:
ball_in_cup = self.check_ball_in_cup(sim, self.ball_collision_id)
self.ball_in_cup = ball_in_cup
if ball_in_cup:
dist_to_ctxt = np.linalg.norm(ball_pos - self.context)
self.dist_ctxt = dist_to_ctxt
if step == self.sim_time - 1:
min_dist = np.min(self.dists)
dist_final = self.dists_final[-1]
# dist_ctxt = self.dists_ctxt[-1]
# cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt)
cost = 2 * (0.5 * min_dist + 0.5 * dist_final + 0.1 * self.dist_ctxt)
reward = np.exp(-1 * cost) - 1e-4 * action_cost
success = dist_final < 0.05 and self.dist_ctxt < 0.05
else:
reward = - 1e-4 * action_cost
success = False
return reward, success, stop_sim
def _get_stage_wise_cost(self, ball_in_cup, min_dist, dist_final, dist_to_ctxt):
if not ball_in_cup:
cost = 3 + 2*(0.5 * min_dist**2 + 0.5 * dist_final**2)
else:
cost = 2 * dist_to_ctxt ** 2
print('Context Distance:', dist_to_ctxt)
return cost
def check_ball_in_cup(self, sim, ball_collision_id):
cup_base_collision_id = sim.model._geom_name2id["cup_base_contact"]
for coni in range(0, sim.data.ncon):
con = sim.data.contact[coni]
collision = con.geom1 == cup_base_collision_id and con.geom2 == ball_collision_id
collision_trans = con.geom1 == ball_collision_id and con.geom2 == cup_base_collision_id
if collision or collision_trans:
return True
return False
def check_collision(self, sim):
for coni in range(0, sim.data.ncon):
con = sim.data.contact[coni]
collision = con.geom1 in self.collision_ids and con.geom2 == self.ball_collision_id
collision_trans = con.geom1 == self.ball_collision_id and con.geom2 in self.collision_ids
if collision or collision_trans:
return True
return False