reference/generated/torchrl.envs.LiberoEnv

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LiberoEnv

torchrl.envs.LiberoEnv(*args, num_workers: int | None = None, num_envs: int | None = None, **kwargs)

LIBERO environment built from a task-suite name and task id.

GitHub: Lifelong-Robot-Learning/LIBERO

Paper: https://arxiv.org/abs/2306.03310 (LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning, Liu et al., 2023)

See LiberoWrapper for the full TensorDict schema and keyword arguments. This constructor builds the underlying OffScreenRenderEnv from the benchmark registry, fetches the task’s language instruction and its fixed initial states (50 per task in the standard suites) and wires them into the init-state control machinery.

Parameters:

• task_suite (str) – the task-suite name, from available_envs (e.g. "libero_spatial", "libero_object", "libero_goal", "libero_10").

• task_id (int) – the task index inside the suite.

Keyword Arguments:

• num_workers (int, optional) – if greater than 1, return a ParallelEnv with num_workers LIBERO workers. Defaults to 1.

• num_envs (int, optional) – alias for num_workers.

• camera_height (int or list of int, optional) – rendered image height. When num_workers > 1, a list dispatches one value per worker and must have length num_workers. Defaults to 256 (the SimpleVLA-RL / OpenVLA-OFT resolution).

• camera_width (int or list of int, optional) – rendered image width. When num_workers > 1, a list dispatches one value per worker and must have length num_workers. Defaults to 256.

• render_gpu_device_id (int or list of int, optional) – GPU device id used by robosuite for offscreen rendering. This is the EGL-visible device id inside the process/container, not necessarily the global CUDA ordinal. Use this to spread LIBERO render workers across multiple GPUs, e.g. worker_idx % num_render_gpus. If omitted, robosuite selects its default device. When num_workers > 1, a list dispatches one value per worker and must have length num_workers. Defaults to None.

• env_kwargs (dict or list of dict, optional) – extra keyword arguments forwarded to OffScreenRenderEnv (e.g. horizon). When num_workers > 1, a list dispatches one dict per worker and must have length num_workers.

• **kwargs – see LiberoWrapper. When num_workers > 1, list-valued keyword arguments are dispatched across workers and must have length num_workers; use tuples for sequence-valued arguments that should be broadcast to every worker (for example proprio_keys).

Examples

>>> from torchrl.envs import LiberoEnv
>>> env = LiberoEnv(
...     "libero_spatial",
...     task_id=0,
...     camera_height=128,
...     camera_width=128,
...     init_state_mode="cycle",
... )
>>> td = env.reset()
>>> td["language_instruction"]
'pick up the black bowl between the plate and the ramekin and place it on the plate'
>>> td["observation", "image"].shape
torch.Size([3, 128, 128])