Fix bugs to create mp environments. Still conflicts with mp_pytorch_lib
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@@ -1,4 +1,3 @@
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from abc import ABC
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from typing import Tuple, Union
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import gym
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@@ -80,7 +79,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
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bc_time=np.zeros((1,)) if not self.replanning_schedule else self.current_traj_steps * self.dt,
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bc_pos=self.current_pos, bc_vel=self.current_vel)
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# TODO: is this correct for replanning? Do we need to adjust anything here?
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self.traj_gen.set_duration(None if self.learn_sub_trajectories else self.duration, np.array([self.dt]))
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self.traj_gen.set_duration(None if self.learn_sub_trajectories else np.array([self.duration]), np.array([self.dt]))
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traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
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trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
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@@ -109,7 +108,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
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""" This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
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# agent to learn when to release the ball
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mp_params, env_spec_params = self._episode_callback(action)
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mp_params, env_spec_params = self.env._episode_callback(action, self.traj_gen)
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trajectory, velocity = self.get_trajectory(mp_params)
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trajectory_length = len(trajectory)
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@@ -1,8 +1,9 @@
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from typing import Union, Tuple
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from mp_pytorch.mp.mp_interfaces import MPInterface
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from abc import abstractmethod
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import gym
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import numpy as np
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from abc import abstractmethod
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class RawInterfaceWrapper(gym.Wrapper):
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@@ -56,7 +57,7 @@ class RawInterfaceWrapper(gym.Wrapper):
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# return bool(self.replanning_model(s))
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return False
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def _episode_callback(self, action: np.ndarray) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
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def _episode_callback(self, action: np.ndarray, traj_gen: MPInterface) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
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"""
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Used to extract the parameters for the motion primitive and other parameters from an action array which might
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include other actions like ball releasing time for the beer pong environment.
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