89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
|
from mp_lib import det_promp
|
||
|
|
import numpy as np
|
||
|
|
import gym
|
||
|
|
|
||
|
|
|
||
|
|
class DetPMPEnvWrapperBase(gym.Wrapper):
|
||
|
|
def __init__(self,
|
||
|
|
env,
|
||
|
|
num_dof,
|
||
|
|
num_basis,
|
||
|
|
width,
|
||
|
|
start_pos=None,
|
||
|
|
duration=1,
|
||
|
|
dt=0.01,
|
||
|
|
post_traj_time=0.,
|
||
|
|
policy=None,
|
||
|
|
weights_scale=1):
|
||
|
|
super(DetPMPEnvWrapperBase, self).__init__(env)
|
||
|
|
self.num_dof = num_dof
|
||
|
|
self.num_basis = num_basis
|
||
|
|
self.dim = num_dof * num_basis
|
||
|
|
self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, width=width, off=0.01)
|
||
|
|
weights = np.zeros(shape=(num_basis, num_dof))
|
||
|
|
self.pmp.set_weights(duration, weights)
|
||
|
|
self.weights_scale = weights_scale
|
||
|
|
|
||
|
|
self.duration = duration
|
||
|
|
self.dt = dt
|
||
|
|
self.post_traj_steps = int(post_traj_time / dt)
|
||
|
|
|
||
|
|
self.start_pos = start_pos
|
||
|
|
|
||
|
|
self.policy = policy
|
||
|
|
|
||
|
|
def __call__(self, params):
|
||
|
|
params = np.atleast_2d(params)
|
||
|
|
observations = []
|
||
|
|
rewards = []
|
||
|
|
dones = []
|
||
|
|
infos = []
|
||
|
|
for p in params:
|
||
|
|
observation, reward, done, info = self.rollout(p)
|
||
|
|
observations.append(observation)
|
||
|
|
rewards.append(reward)
|
||
|
|
dones.append(done)
|
||
|
|
infos.append(info)
|
||
|
|
|
||
|
|
return np.array(rewards), infos
|
||
|
|
|
||
|
|
def rollout(self, params, render=False):
|
||
|
|
""" This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
|
||
|
|
class DetPMPEnvWrapperPD(DetPMPEnvWrapperBase):
|
||
|
|
"""
|
||
|
|
Wrapper for gym environments which creates a trajectory in joint velocity space
|
||
|
|
"""
|
||
|
|
def rollout(self, params, render=False):
|
||
|
|
params = np.reshape(params, newshape=(self.num_basis, self.num_dof)) * self.weights_scale
|
||
|
|
self.pmp.set_weights(self.duration, params)
|
||
|
|
t, des_pos, des_vel, des_acc = self.pmp.compute_trajectory(1/self.dt, 1.)
|
||
|
|
des_pos += self.start_pos[None, :]
|
||
|
|
|
||
|
|
if self.post_traj_steps > 0:
|
||
|
|
des_pos = np.vstack([des_pos, np.tile(des_pos[-1, :], [self.post_traj_steps, 1])])
|
||
|
|
des_vel = np.vstack([des_vel, np.zeros(shape=(self.post_traj_steps, self.num_dof))])
|
||
|
|
|
||
|
|
self._trajectory = des_pos
|
||
|
|
|
||
|
|
rews = []
|
||
|
|
infos = []
|
||
|
|
|
||
|
|
self.env.reset()
|
||
|
|
|
||
|
|
for t, pos_vel in enumerate(zip(des_pos, des_vel)):
|
||
|
|
ac = self.policy.get_action(self.env, pos_vel[0], pos_vel[1])
|
||
|
|
obs, rew, done, info = self.env.step(ac)
|
||
|
|
rews.append(rew)
|
||
|
|
infos.append(info)
|
||
|
|
if render:
|
||
|
|
self.env.render(mode="human")
|
||
|
|
if done:
|
||
|
|
break
|
||
|
|
|
||
|
|
reward = np.sum(rews)
|
||
|
|
|
||
|
|
return obs, reward, done, info
|