55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
import gym
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import numpy as np
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from mp_lib import det_promp
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from alr_envs.utils.mps.alr_env import AlrEnv
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from alr_envs.utils.mps.mp_wrapper import MPWrapper
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class DetPMPWrapper(MPWrapper):
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def __init__(self, env: AlrEnv, num_dof, num_basis, width, start_pos=None, duration=1, post_traj_time=0.,
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policy_type=None, weights_scale=1, zero_start=False, zero_goal=False, learn_mp_length: bool =True,
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**mp_kwargs):
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self.duration = duration # seconds
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super().__init__(env=env, num_dof=num_dof, duration=duration, post_traj_time=post_traj_time,
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policy_type=policy_type, weights_scale=weights_scale, num_basis=num_basis,
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width=width, zero_start=zero_start, zero_goal=zero_goal,
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**mp_kwargs)
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self.learn_mp_length = learn_mp_length
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if self.learn_mp_length:
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parameter_space_shape = (1+num_basis*num_dof,)
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else:
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parameter_space_shape = (num_basis * num_dof,)
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self.min_param = -np.inf
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self.max_param = np.inf
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self.parameterization_space = gym.spaces.Box(low=self.min_param, high=self.max_param,
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shape=parameter_space_shape, dtype=np.float32)
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self.start_pos = start_pos
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def initialize_mp(self, num_dof: int, duration: int, num_basis: int = 5, width: float = None,
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zero_start: bool = False, zero_goal: bool = False, **kwargs):
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pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01,
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zero_start=zero_start, zero_goal=zero_goal)
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weights = np.zeros(shape=(num_basis, num_dof))
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pmp.set_weights(duration, weights)
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return pmp
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def mp_rollout(self, action):
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if self.learn_mp_length:
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duration = max(1, self.duration*abs(action[0]))
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params = np.reshape(action[1:], (self.mp.n_basis, -1)) * self.weights_scale # TODO: Fix Bug when zero_start is true
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else:
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duration = self.duration
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params = np.reshape(action, (self.mp.n_basis, -1)) * self.weights_scale # TODO: Fix Bug when zero_start is true
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self.mp.set_weights(1., params)
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_, des_pos, des_vel, _ = self.mp.compute_trajectory(frequency=max(1, duration))
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if self.mp.zero_start:
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des_pos += self.start_pos
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return des_pos, des_vel
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