49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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from typing import Tuple, Union
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import numpy as np
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from mp_env_api import MPEnvWrapper
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class MPWrapper(MPEnvWrapper):
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@property
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def active_obs(self):
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# This structure is the same for all metaworld environments.
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# Only the observations which change could differ
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return np.hstack([
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# Current observation
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[False] * 3, # end-effector position
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[False] * 1, # normalized gripper open distance
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[True] * 3, # main object position
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[False] * 4, # main object quaternion
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[False] * 3, # secondary object position
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[False] * 4, # secondary object quaternion
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# Previous observation
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# TODO: Include previous values? According to their source they might be wrong for the first iteration.
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[False] * 3, # previous end-effector position
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[False] * 1, # previous normalized gripper open distance
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[False] * 3, # previous main object position
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[False] * 4, # previous main object quaternion
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[False] * 3, # previous second object position
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[False] * 4, # previous second object quaternion
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# Goal
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[True] * 3, # goal position
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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.dt
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