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95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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import gymnasium as gym
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from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, LiberoEnv, PushtEnv, XarmEnv
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def make_env_config(env_type: str, **kwargs) -> EnvConfig:
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if env_type == "aloha":
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return AlohaEnv(**kwargs)
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elif env_type == "pusht":
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return PushtEnv(**kwargs)
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elif env_type == "xarm":
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return XarmEnv(**kwargs)
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elif env_type == "hil":
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return HILEnvConfig(**kwargs)
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elif env_type == "libero":
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return LiberoEnv(**kwargs)
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else:
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raise ValueError(f"Policy type '{env_type}' is not available.")
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def make_env(
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cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False
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) -> gym.vector.VectorEnv | dict[str, dict[int, gym.vector.VectorEnv]]:
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"""Makes a gym vector environment according to the config.
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Args:
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cfg (EnvConfig): the config of the environment to instantiate.
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n_envs (int, optional): The number of parallelized env to return. Defaults to 1.
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use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to
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False.
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Raises:
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ValueError: if n_envs < 1
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ModuleNotFoundError: If the requested env package is not installed
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Returns:
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gym.vector.VectorEnv: The parallelized gym.env instance.
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dict[str, dict[int, gym.vector.VectorEnv]]: A mapping from task suite
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names to indexed vectorized environments (when multitask eval is used).
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"""
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if n_envs < 1:
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raise ValueError("`n_envs` must be at least 1")
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env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
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if "libero" in cfg.type:
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from lerobot.envs.libero import create_libero_envs
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return create_libero_envs(
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task=cfg.task,
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n_envs=n_envs,
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camera_name=cfg.camera_name,
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init_states=cfg.init_states,
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gym_kwargs=cfg.gym_kwargs,
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env_cls=env_cls,
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multitask_eval=cfg.multitask_eval,
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)
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package_name = f"gym_{cfg.type}"
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try:
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importlib.import_module(package_name)
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except ModuleNotFoundError as e:
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raise ModuleNotFoundError(
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f'{package_name} is not installed. Install with: pip install "lerobot[{cfg.type}]"'
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) from e
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gym_handle = f"{package_name}/{cfg.task}"
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def _make_one():
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return gym.make(gym_handle, disable_env_checker=True, **(cfg.gym_kwargs or {}))
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vec = env_cls([_make_one for _ in range(n_envs)])
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# normalize to {suite: {task_id: vec_env}} for consistency
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suite_name = cfg.type # e.g., "pusht", "aloha"
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return {suite_name: {0: vec}}
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