61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
from typing import Tuple, Union
|
|
|
|
import numpy as np
|
|
|
|
from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
|
|
|
|
|
|
class MPWrapper(RawInterfaceWrapper):
|
|
mp_config = {
|
|
'ProMP': {
|
|
'controller_kwargs': {
|
|
'p_gains': 50.0,
|
|
},
|
|
'trajectory_generator_kwargs': {
|
|
'weights_scale': 0.2,
|
|
},
|
|
},
|
|
'DMP': {
|
|
'controller_kwargs': {
|
|
'p_gains': 50.0,
|
|
},
|
|
'phase_generator': {
|
|
'alpha_phase': 2,
|
|
},
|
|
'trajectory_generator_kwargs': {
|
|
'weights_scale': 500,
|
|
},
|
|
},
|
|
'ProDMP': {},
|
|
}
|
|
|
|
@property
|
|
def context_mask(self) -> np.ndarray:
|
|
# Joint and target positions are randomized, velocities are always set to 0.
|
|
return np.hstack([
|
|
[True] * 3, # target position
|
|
[True] * 12, # sin/cos arm joint position
|
|
[True] * 6, # arm joint torques
|
|
[False] * 6, # arm joint velocities
|
|
[True] * 3, # sin/cos hand joint position
|
|
[False] * 3, # hand joint velocities
|
|
[True] * 3, # hand pinch site position
|
|
[True] * 9, # pinch site rmat
|
|
])
|
|
|
|
@property
|
|
def current_pos(self) -> Union[float, int, np.ndarray]:
|
|
return self.env.physics.named.data.qpos[:]
|
|
|
|
@property
|
|
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
|
|
return self.env.physics.named.data.qvel[:]
|
|
|
|
@property
|
|
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
|
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
|
|
|
|
@property
|
|
def dt(self) -> Union[float, int]:
|
|
return self.env.control_timestep()
|