fancy_gym/fancy_gym/dmc/suite/ball_in_cup/mp_wrapper.py

54 lines
1.6 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,
},
},
'DMP': {
'controller_kwargs': {
'p_gains': 50.0,
},
'phase_generator': {
'alpha_phase': 2,
},
'trajectory_generator_kwargs': {
'weights_scale': 10
},
},
'ProDMP': {},
}
@property
def context_mask(self) -> np.ndarray:
# Besides the ball position, the environment is always set to 0.
return np.hstack([
[False] * 2, # cup position
[True] * 2, # ball position
[False] * 2, # cup velocity
[False] * 2, # ball velocity
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return np.hstack([self.env.physics.named.data.qpos['cup_x'], self.env.physics.named.data.qpos['cup_z']])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return np.hstack([self.env.physics.named.data.qvel['cup_x'], self.env.physics.named.data.qvel['cup_z']])
@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()