e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
144 lines
4.6 KiB
Python
144 lines
4.6 KiB
Python
"""
|
|
Environment wrapper for Robomimic environments with state observations.
|
|
|
|
Also return done=False since we do not terminate episode early.
|
|
|
|
Modified from https://github.com/real-stanford/diffusion_policy/blob/main/diffusion_policy/env/robomimic/robomimic_lowdim_wrapper.py
|
|
|
|
For consistency, we will use Dict{} for the observation space, with the key "state" for the state observation.
|
|
"""
|
|
|
|
import numpy as np
|
|
import gym
|
|
from gym import spaces
|
|
import imageio
|
|
|
|
|
|
class RobomimicLowdimWrapper(gym.Env):
|
|
def __init__(
|
|
self,
|
|
env,
|
|
normalization_path=None,
|
|
low_dim_keys=[
|
|
"robot0_eef_pos",
|
|
"robot0_eef_quat",
|
|
"robot0_gripper_qpos",
|
|
"object",
|
|
],
|
|
clamp_obs=False,
|
|
init_state=None,
|
|
render_hw=(256, 256),
|
|
render_camera_name="agentview",
|
|
):
|
|
self.env = env
|
|
self.init_state = init_state
|
|
self.render_hw = render_hw
|
|
self.render_camera_name = render_camera_name
|
|
self.video_writer = None
|
|
self.clamp_obs = clamp_obs
|
|
|
|
# set up normalization
|
|
self.normalize = normalization_path is not None
|
|
if self.normalize:
|
|
normalization = np.load(normalization_path)
|
|
self.obs_min = normalization["obs_min"]
|
|
self.obs_max = normalization["obs_max"]
|
|
self.action_min = normalization["action_min"]
|
|
self.action_max = normalization["action_max"]
|
|
|
|
# setup spaces
|
|
low = np.full(env.action_dimension, fill_value=-1)
|
|
high = np.full(env.action_dimension, fill_value=1)
|
|
self.action_space = gym.spaces.Box(
|
|
low=low,
|
|
high=high,
|
|
shape=low.shape,
|
|
dtype=low.dtype,
|
|
)
|
|
self.obs_keys = low_dim_keys
|
|
self.observation_space = spaces.Dict()
|
|
obs_example_full = self.env.get_observation()
|
|
obs_example = np.concatenate(
|
|
[obs_example_full[key] for key in self.obs_keys], axis=0
|
|
)
|
|
low = np.full_like(obs_example, fill_value=-1)
|
|
high = np.full_like(obs_example, fill_value=1)
|
|
self.observation_space["state"] = spaces.Box(
|
|
low=low,
|
|
high=high,
|
|
shape=low.shape,
|
|
dtype=np.float32,
|
|
)
|
|
|
|
def normalize_obs(self, obs):
|
|
obs = 2 * (
|
|
(obs - self.obs_min) / (self.obs_max - self.obs_min + 1e-6) - 0.5
|
|
) # -> [-1, 1]
|
|
if self.clamp_obs:
|
|
obs = np.clip(obs, -1, 1)
|
|
return obs
|
|
|
|
def unnormalize_action(self, action):
|
|
action = (action + 1) / 2 # [-1, 1] -> [0, 1]
|
|
return action * (self.action_max - self.action_min) + self.action_min
|
|
|
|
def get_observation(self, raw_obs):
|
|
obs = {"state": np.concatenate([raw_obs[key] for key in self.obs_keys], axis=0)}
|
|
if self.normalize:
|
|
obs["state"] = self.normalize_obs(obs["state"])
|
|
return obs
|
|
|
|
def seed(self, seed=None):
|
|
if seed is not None:
|
|
np.random.seed(seed=seed)
|
|
else:
|
|
np.random.seed()
|
|
|
|
def reset(self, options={}, **kwargs):
|
|
"""Ignore passed-in arguments like seed"""
|
|
# Close video if exists
|
|
if self.video_writer is not None:
|
|
self.video_writer.close()
|
|
self.video_writer = None
|
|
|
|
# Start video if specified
|
|
if "video_path" in options:
|
|
self.video_writer = imageio.get_writer(options["video_path"], fps=30)
|
|
|
|
# Call reset
|
|
new_seed = options.get(
|
|
"seed", None
|
|
) # used to set all environments to specified seeds
|
|
if self.init_state is not None:
|
|
# always reset to the same state to be compatible with gym
|
|
raw_obs = self.env.reset_to({"states": self.init_state})
|
|
elif new_seed is not None:
|
|
self.seed(seed=new_seed)
|
|
raw_obs = self.env.reset()
|
|
else:
|
|
# random reset
|
|
raw_obs = self.env.reset()
|
|
return self.get_observation(raw_obs)
|
|
|
|
def step(self, action):
|
|
if self.normalize:
|
|
action = self.unnormalize_action(action)
|
|
raw_obs, reward, done, info = self.env.step(action)
|
|
obs = self.get_observation(raw_obs)
|
|
|
|
# render if specified
|
|
if self.video_writer is not None:
|
|
video_img = self.render(mode="rgb_array")
|
|
self.video_writer.append_data(video_img)
|
|
|
|
return obs, reward, False, info
|
|
|
|
def render(self, mode="rgb_array"):
|
|
h, w = self.render_hw
|
|
return self.env.render(
|
|
mode=mode,
|
|
height=h,
|
|
width=w,
|
|
camera_name=self.render_camera_name,
|
|
)
|