LIBERO

Lifelong robot learning benchmark: 130 simulated manipulation tasks in five suites, 6,500 human-teleoperated demonstrations, and a controlled design where three of the suites vary exactly one factor — spatial layout, objects, or goal — against an otherwise fixed setup.

LIBERO is a benchmark for knowledge transfer in lifelong robot learning, and its defining feature is experimental control rather than scale. Three of its suites hold everything fixed except one factor, so a policy's failure can be attributed to a specific kind of distribution shift instead of to "the scene changed".

The five suites.

• LIBERO-Spatial (10 tasks) — identical objects, different spatial relationships. The instruction is what disambiguates them: pick up the black bowl next to the cookie box vs …on the ramekin.
• LIBERO-Object (10 tasks) — identical layout, a different object each time.
• LIBERO-Goal (10 tasks) — identical objects and layout, a different goal each time.
• LIBERO-Long / LIBERO-10 (10 tasks) — long-horizon tasks that entangle all three factors, e.g. turn on the stove and put the moka pot on it.
• LIBERO-90 (90 tasks) — a broad pretraining pool drawn from kitchen, living-room and study scenes. LIBERO-90 and LIBERO-Long together are referred to as LIBERO-100.

What is actually in the release. Exactly 50 human-teleoperated demonstrations per task — verified file by file, not assumed — giving 6,500 demonstrations and 1,007,618 transitions across 130 HDF5 files totalling 93.5 GiB. At the 20 Hz control rate that is about 14 hours of interaction. Tasks are defined in PDDL-style BDDL files, so the suite is designed to be extended rather than frozen.

Observations and actions. Each timestep carries an `agentview_rgb` and an `eye_in_hand_rgb` frame at 128×128, plus `joint_states` (7), `gripper_states` (2), `ee_pos` (3), `ee_ori` (3) and `ee_states` (6). Actions are 7-D OSC_POSE deltas. Full MuJoCo simulator state (`states`, 92-D) and the per-demo `init_state` are stored alongside, so any episode can be replayed deterministically — which is what every downstream re-render depends on.

Which copy to use. This is worth knowing before downloading 93 GiB. The canonical HDF5 release is the one described above. Most vision-language-action work does not train on it directly, but on OpenVLA's regenerated `modified_libero_rlds`, which replays the demos in simulation and drops no-op-only and unsuccessful episodes — retaining 1,693 of 2,000 episodes (84.7%) across Spatial, Object, Goal and Long. NVIDIA's LeRobot v3 conversion applies the same filtering and additionally covers LIBERO-90, keeping 5,614 of 6,500 episodes (86.4%) and 842,714 of 1,007,618 frames. Physical Intelligence's LeRobot v2 copy is the four-suite filtered set (1,693 episodes / 273,465 frames). Numbers reported against "LIBERO" are usually against a filtered subset, not this one.

Licensing. The authors' README licenses the codebase under MIT and the datasets under CC BY 4.0; the Hugging Face data mirror is labelled Apache-2.0. The CC BY 4.0 statement is the one that speaks specifically to the data and is used here.