small bp and tt updates
This commit is contained in:
Maximilian Huettenrauch
parent
a0af743585
commit
92d05a9dfd
@ -204,7 +204,7 @@ register(id='TableTennis2DCtxt-v0',
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register(id='TableTennis2DCtxt-v1',
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entry_point='alr_envs.alr.mujoco:TTEnvGym',
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max_episode_steps=1750,
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max_episode_steps=MAX_EPISODE_STEPS,
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kwargs={'ctxt_dim': 2, 'fixed_goal': True})
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register(id='TableTennis4DCtxt-v0',
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@ -365,11 +365,14 @@ for _v in _versions:
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ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
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## Beerpong
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register(
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id='BeerpongProMP-v0',
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_versions = ["v0", "v1", "v2", "v3"]
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for _v in _versions:
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_env_id = f'BeerpongProMP-{_v}'
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register(
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id=_env_id,
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entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
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kwargs={
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"name": "alr_envs:ALRBeerPong-v0",
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"name": f"alr_envs:ALRBeerPong-{_v}",
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"wrappers": [mujoco.beerpong.MPWrapper],
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"mp_kwargs": {
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"num_dof": 7,
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@ -377,7 +380,7 @@ register(
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"duration": 1,
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"post_traj_time": 2,
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"policy_type": "motor",
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"weights_scale": 0.2,
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"weights_scale": 1,
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"zero_start": True,
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"zero_goal": False,
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"policy_kwargs": {
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@ -386,8 +389,8 @@ register(
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}
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}
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}
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)
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ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("BeerpongProMP-v0")
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)
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ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
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## Table Tennis
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ctxt_dim = [2, 4]
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@ -429,7 +432,9 @@ register(
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"duration": 1.,
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"post_traj_time": 2.5,
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"policy_type": "motor",
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"weights_scale": 0.2,
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"weights_scale": 1,
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"off": -0.05,
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"bandwidth_factor": 3.5,
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"zero_start": True,
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"zero_goal": False,
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"policy_kwargs": {
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@ -127,13 +127,14 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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self._steps += 1
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else:
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reward = -30
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reward_infos = dict()
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success = False
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is_collided = False
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done = True
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ball_pos = np.zeros(3)
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ball_vel = np.zeros(3)
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return ob, reward, done, dict(reward_dist=reward_dist,
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infos = dict(reward_dist=reward_dist,
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reward_ctrl=reward_ctrl,
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reward=reward,
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velocity=angular_vel,
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@ -145,6 +146,9 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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ball_vel=ball_vel,
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success=success,
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is_collided=is_collided, sim_crash=crash)
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infos.update(reward_infos)
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return ob, reward, done, infos
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def check_traj_in_joint_limits(self):
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return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min)
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@ -110,7 +110,7 @@ class BeerPongReward:
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success = ball_in_cup
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crash = self._is_collided
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else:
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reward = - 1e-4 * action_cost
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reward = - 1e-2 * action_cost
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success = False
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crash = False
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@ -10,7 +10,7 @@ from alr_envs.alr.mujoco.table_tennis.tt_reward import TT_Reward
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#TODO: Check for simulation stability. Make sure the code runs even for sim crash
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MAX_EPISODE_STEPS = 2875
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MAX_EPISODE_STEPS = 1750
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BALL_NAME_CONTACT = "target_ball_contact"
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BALL_NAME = "target_ball"
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TABLE_NAME = 'table_tennis_table'
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@ -42,9 +42,10 @@ class TTEnvGym(MujocoEnv, utils.EzPickle):
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else:
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raise ValueError("either 2 or 4 dimensional Contexts available")
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action_space_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2])
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action_space_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2])
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self.action_space = spaces.Box(low=action_space_low, high=action_space_high, dtype='float64')
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# has no effect as it is overwritten in init of super
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# action_space_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2])
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# action_space_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2])
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# self.action_space = spaces.Box(low=action_space_low, high=action_space_high, dtype='float64')
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self.time_steps = 0
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self.init_qpos_tt = np.array([0, 0, 0, 1.5, 0, 0, 1.5, 0, 0, 0])
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@ -159,7 +160,10 @@ class TTEnvGym(MujocoEnv, utils.EzPickle):
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done = True
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reward = -25
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ob = self._get_obs()
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return ob, reward, done, {"hit_ball": self.hit_ball} # might add some information here ....
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info = {"hit_ball": self.hit_ball,
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"q_pos": np.copy(self.sim.data.qpos[:7]),
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"ball_pos": np.copy(self.sim.data.qpos[7:])}
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return ob, reward, done, info # might add some information here ....
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def set_context(self, context):
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old_state = self.sim.get_state()
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@ -19,7 +19,7 @@ class TT_Reward:
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# # seems to work for episodic case
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min_r_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj) - np.array(self.c_racket_traj), axis=1))
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if not hited_ball:
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return 0.2 * (1- np.tanh(min_r_b_dist**2))
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return 0.2 * (1 - np.tanh(min_r_b_dist**2))
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else:
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if ball_landing_pos is None:
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min_b_des_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj)[:,:2] - self.c_goal[:2], axis=1))
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