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#!/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}}
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