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ported mp_config for mujoco/table_tennis

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Dominik Moritz Roth 2023-07-23 10:25:01 +02:00
parent 64e6ac5323
commit 9ba3fa9dbc
1 changed files with 100 additions and 2 deletions
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98
fancy_gym/envs/mujoco/table_tennis/mp_wrapper.py
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@ -7,6 +7,53 @@ from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import jnt_pos_low, j
class TT_MPWrapper(RawInterfaceWrapper): class TT_MPWrapper(RawInterfaceWrapper):
mp_config = {
'ProMP': {
'phase_generator_kwargs': {
'learn_tau': False,
'learn_delay': False,
'tau_bound': [0.8, 1.5],
'delay_bound': [0.05, 0.15],
},
'controller_kwargs': {
'p_gains': 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
'd_gains': 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]),
},
'basis_generator_kwargs': {
'num_basis': 3,
'num_basis_zero_start': 1,
'num_basis_zero_goal': 1,
},
'black_box_kwargs': {
'verbose': 2,
},
},
'DMP': {},
'ProDMP': {
'phase_generator_kwargs': {
'learn_tau': True,
'learn_delay': True,
'tau_bound': [0.8, 1.5],
'delay_bound': [0.05, 0.15],
'alpha_phase': 3,
},
'controller_kwargs': {
'p_gains': 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
'd_gains': 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]),
},
'basis_generator_kwargs': {
'num_basis': 3,
'alpha': 25,
'basis_bandwidth_factor': 3,
},
'trajectory_generator_kwargs': {
'weights_scale': 0.7,
'auto_scale_basis': True,
'relative_goal': True,
'disable_goal': True,
},
},
}
# Random x goal + random init pos # Random x goal + random init pos
@property @property
@ -39,7 +86,58 @@ class TT_MPWrapper(RawInterfaceWrapper):
return_contextual_obs: bool) -> Tuple[np.ndarray, float, bool, dict]: return_contextual_obs: bool) -> Tuple[np.ndarray, float, bool, dict]:
return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs) return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs)
class TT_MPWrapper_Replan(TT_MPWrapper):
mp_config = {
'ProMP': {},
'DMP': {},
'ProDMP': {
'phase_generator_kwargs': {
'learn_tau': True,
'learn_delay': True,
'tau_bound': [0.8, 1.5],
'delay_bound': [0.05, 0.15],
'alpha_phase': 3,
},
'controller_kwargs': {
'p_gains': 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
'd_gains': 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]),
},
'basis_generator_kwargs': {
'num_basis': 2,
'alpha': 25,
'basis_bandwidth_factor': 3,
},
'trajectory_generator_kwargs': {
'auto_scale_basis': True,
'goal_offset': 1.0,
},
'black_box_kwargs': {
'max_planning_times': 3,
'replanning_schedule': lambda pos, vel, obs, action, t: t % 50 == 0,
},
},
}
class TTVelObs_MPWrapper(TT_MPWrapper): class TTVelObs_MPWrapper(TT_MPWrapper):
# Will inherit mp_config from TT_MPWrapper
@property
def context_mask(self):
return np.hstack([
[False] * 7, # joints position
[False] * 7, # joints velocity
[True] * 2, # position ball x, y
[False] * 1, # position ball z
[True] * 3, # velocity ball x, y, z
[True] * 2, # target landing position
# [True] * 1, # time
])
class TTVelObs_MPWrapper_Replan(TT_MPWrapper_Replan):
# Will inherit mp_config from TT_MPWrapper_Replan
@property @property
def context_mask(self): def context_mask(self):