50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
from typing import 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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return np.hstack([
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[False] * 3, # achieved goal
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[True] * 3, # desired/true goal
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[False] * 3, # grip pos
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[True, True, False] * int(self.has_object), # object position
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[True, True, False] * int(self.has_object), # object relative position
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[False] * 2, # gripper state
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[False] * 3 * int(self.has_object), # object rotation
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[False] * 3 * int(self.has_object), # object velocity position
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[False] * 3 * int(self.has_object), # object velocity rotation
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[False] * 3, # grip velocity position
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[False] * 2, # gripper velocity
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]).astype(bool)
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@property
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def current_vel(self) -> Union[float, int, np.ndarray]:
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dt = self.sim.nsubsteps * self.sim.model.opt.timestep
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grip_velp = self.sim.data.get_site_xvelp("robot0:grip") * dt
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# gripper state should be symmetric for left and right.
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# They are controlled with only one action for both gripper joints
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gripper_state = self.sim.data.get_joint_qvel('robot0:r_gripper_finger_joint') * dt
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return np.hstack([grip_velp, gripper_state])
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@property
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def current_pos(self) -> Union[float, int, np.ndarray]:
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grip_pos = self.sim.data.get_site_xpos("robot0:grip")
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# gripper state should be symmetric for left and right.
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# They are controlled with only one action for both gripper joints
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gripper_state = self.sim.data.get_joint_qpos('robot0:r_gripper_finger_joint')
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return np.hstack([grip_pos, gripper_state])
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@property
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def goal_pos(self):
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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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