#!/usr/bin/env python # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import gymnasium as gym from gymnasium.envs.registration import registry as gym_registry from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv def make_env_config(env_type: str, **kwargs) -> EnvConfig: if env_type == "aloha": return AlohaEnv(**kwargs) elif env_type == "pusht": return PushtEnv(**kwargs) elif env_type == "libero": return LiberoEnv(**kwargs) else: raise ValueError(f"Policy type '{env_type}' is not available.") def make_env( cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False ) -> dict[str, dict[int, gym.vector.VectorEnv]]: """Makes a gym vector environment according to the config. Args: cfg (EnvConfig): the config of the environment to instantiate. n_envs (int, optional): The number of parallelized env to return. Defaults to 1. use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to False. Raises: ValueError: if n_envs < 1 ModuleNotFoundError: If the requested env package is not installed Returns: dict[str, dict[int, gym.vector.VectorEnv]]: A mapping from suite name to indexed vectorized environments. - For multi-task benchmarks (e.g., LIBERO): one entry per suite, and one vec env per task_id. - For single-task environments: a single suite entry (cfg.type) with task_id=0. """ if n_envs < 1: raise ValueError("`n_envs` must be at least 1") env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv if "libero" in cfg.type: from lerobot.envs.libero import create_libero_envs if cfg.task is None: raise ValueError("LiberoEnv requires a task to be specified") return create_libero_envs( task=cfg.task, n_envs=n_envs, camera_name=cfg.camera_name, init_states=cfg.init_states, gym_kwargs=cfg.gym_kwargs, env_cls=env_cls, ) elif "metaworld" in cfg.type: from lerobot.envs.metaworld import create_metaworld_envs if cfg.task is None: raise ValueError("MetaWorld requires a task to be specified") return create_metaworld_envs( task=cfg.task, n_envs=n_envs, gym_kwargs=cfg.gym_kwargs, env_cls=env_cls, ) if cfg.gym_id not in gym_registry: print(f"gym id '{cfg.gym_id}' not found, attempting to import '{cfg.package_name}'...") try: importlib.import_module(cfg.package_name) except ModuleNotFoundError as e: raise ModuleNotFoundError( f"Package '{cfg.package_name}' required for env '{cfg.type}' not found. " f"Please install it or check PYTHONPATH." ) from e if cfg.gym_id not in gym_registry: raise gym.error.NameNotFound( f"Environment '{cfg.gym_id}' not registered even after importing '{cfg.package_name}'." ) def _make_one(): return gym.make(cfg.gym_id, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {})) vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=gym.vector.AutoresetMode.SAME_STEP) # normalize to {suite: {task_id: vec_env}} for consistency suite_name = cfg.type # e.g., "pusht", "aloha" return {suite_name: {0: vec}}