58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
from typing import Tuple, Union
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import numpy as np
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from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
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class MPWrapper(RawInterfaceWrapper):
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mp_config = {
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'ProMP': {
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'controller_kwargs': {
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'p_gains': 50.0,
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'd_gains': 1.0,
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},
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'trajectory_generator_kwargs': {
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'weights_scale': 0.2,
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},
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},
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'DMP': {
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'controller_kwargs': {
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'p_gains': 50.0,
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'd_gains': 1.0,
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},
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'phase_generator': {
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'alpha_phase': 2,
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},
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'trajectory_generator_kwargs': {
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'weights_scale': 500,
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},
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},
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'ProDMP': {},
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}
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@property
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def context_mask(self) -> np.ndarray:
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# Joint and target positions are randomized, velocities are always set to 0.
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return np.hstack([
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[True] * 2, # joint position
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[True] * 2, # target position
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[False] * 2, # joint velocity
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])
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@property
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def current_pos(self) -> Union[float, int, np.ndarray]:
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return self.env.physics.named.data.qpos[:]
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@property
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def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
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return self.env.physics.named.data.qvel[:]
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@property
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def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
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raise ValueError("Goal position is not available and has to be learnt based on the environment.")
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@property
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def dt(self) -> Union[float, int]:
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return self.env.control_timestep()
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