fancy_gym/fancy_gym/envs/mujoco/box_pushing/mp_wrapper.py

73 lines
2.2 KiB
Python

from typing import Union, Tuple
import numpy as np
from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
class MPWrapper(RawInterfaceWrapper):
mp_config = {
'ProMP': {
'controller_kwargs': {
'p_gains': 0.01 * np.array([120., 120., 120., 120., 50., 30., 10.]),
'd_gains': 0.01 * np.array([10., 10., 10., 10., 6., 5., 3.]),
},
'basis_generator_kwargs': {
'basis_bandwidth_factor': 2 # 3.5, 4 to try
}
},
'DMP': {},
'ProDMP': {},
}
# Random x goal + random init pos
@property
def context_mask(self):
return np.hstack([
[False] * 7, # joints position
[False] * 7, # joints velocity
[False] * 3, # position of box
[False] * 4, # orientation of box
[True] * 3, # position of target
[True] * 4, # orientation of target
# [True] * 1, # time
])
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.data.qpos[:7].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.data.qvel[:7].copy()
class ReplanMPWrapper(MPWrapper):
mp_config = {
'ProMP': {},
'DMP': {},
'ProDMP': {
'controller_kwargs': {
'p_gains': 0.01 * np.array([120., 120., 120., 120., 50., 30., 10.]),
'd_gains': 0.01 * np.array([10., 10., 10., 10., 6., 5., 3.]),
},
'trajectory_generator_kwargs': {
'weights_scale': 0.3,
'goal_scale': 0.3,
'auto_scale_basis': True,
'goal_offset': 1.0,
'disable_goal': True,
},
'basis_generator_kwargs': {
'num_basis': 5,
'basis_bandwidth_factor': 3,
'alpha_phase': 3,
},
'black_box_kwargs': {
'max_planning_times': 4,
'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0,
'condition_on_desired': True,
}
}
}