# VLABench [VLABench](https://github.com/OpenMOSS/VLABench) is a large-scale benchmark for **language-conditioned robotic manipulation with long-horizon reasoning**. The upstream suite covers 100 task categories across 2,000+ objects and evaluates six dimensions of robot intelligence: mesh & texture understanding, spatial reasoning, world-knowledge transfer, semantic instruction comprehension, physical-law understanding, and long-horizon planning. Built on MuJoCo / dm_control with a Franka Panda 7-DOF arm. LeRobot exposes **43 of these tasks** through `--env.task` (21 primitives + 22 composites, see [Available tasks](#available-tasks) below). - Paper: [VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning](https://arxiv.org/abs/2412.18194) - GitHub: [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench) - Project website: [vlabench.github.io](https://vlabench.github.io) - Pretrained policy: [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench) VLABench benchmark overview ## Available tasks VLABench ships two task suites covering **43 task categories** in LeRobot's `--env.task` surface: | Suite | CLI name | Tasks | Description | | --------- | ----------- | ----- | ---------------------------------------------------------------- | | Primitive | `primitive` | 21 | Single / few-skill combinations (select, insert, physics QA) | | Composite | `composite` | 22 | Multi-step reasoning and long-horizon planning (cook, rearrange) | **Primitive tasks:** `select_fruit`, `select_toy`, `select_chemistry_tube`, `add_condiment`, `select_book`, `select_painting`, `select_drink`, `insert_flower`, `select_billiards`, `select_ingredient`, `select_mahjong`, `select_poker`, and physical-reasoning tasks (`density_qa`, `friction_qa`, `magnetism_qa`, `reflection_qa`, `simple_cuestick_usage`, `simple_seesaw_usage`, `sound_speed_qa`, `thermal_expansion_qa`, `weight_qa`). **Composite tasks:** `cluster_billiards`, `cluster_book`, `cluster_drink`, `cluster_toy`, `cook_dishes`, `cool_drink`, `find_unseen_object`, `get_coffee`, `hammer_nail`, `heat_food`, `make_juice`, `play_mahjong`, `play_math_game`, `play_poker`, `play_snooker`, `rearrange_book`, `rearrange_chemistry_tube`, `set_dining_table`, `set_study_table`, `store_food`, `take_chemistry_experiment`, `use_seesaw_complex`. `--env.task` accepts three forms: - a single task name (`select_fruit`) - a comma-separated list (`select_fruit,heat_food`) - a suite shortcut (`primitive`, `composite`, or `primitive,composite`) ## Installation VLABench is **not on PyPI** — its only distribution is the [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench) GitHub repo — so LeRobot does not expose a `vlabench` extra. Install it manually as an editable clone, alongside the MuJoCo / dm_control pins VLABench needs, then fetch the mesh assets: ```bash # After following the standard LeRobot installation instructions. git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms pip install -e ~/VLABench -e ~/rrt-algorithms pip install "mujoco==3.2.2" "dm-control==1.0.22" \ open3d colorlog scikit-learn openai gdown python ~/VLABench/scripts/download_assets.py ``` VLABench requires Linux (`sys_platform == 'linux'`) and Python 3.10+. Set the MuJoCo rendering backend before running: ```bash export MUJOCO_GL=egl # for headless servers (HPC, cloud) ``` ## Evaluation All eval snippets below mirror the command CI runs (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps VLABench's `image` / `second_image` / `wrist_image` camera keys onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_vlabench` policy was trained on. ### Single-task evaluation (recommended for quick iteration) ```bash lerobot-eval \ --policy.path=lerobot/smolvla_vlabench \ --env.type=vlabench \ --env.task=select_fruit \ --eval.batch_size=1 \ --eval.n_episodes=10 \ --eval.use_async_envs=false \ --policy.device=cuda \ '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}' ``` ### Multi-task evaluation Pass a comma-separated list of tasks: ```bash lerobot-eval \ --policy.path=lerobot/smolvla_vlabench \ --env.type=vlabench \ --env.task=select_fruit,select_toy,add_condiment,heat_food \ --eval.batch_size=1 \ --eval.n_episodes=10 \ --eval.use_async_envs=false \ --policy.device=cuda \ '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}' ``` ### Suite-wide evaluation Run an entire suite (all 21 primitives or all 22 composites): ```bash lerobot-eval \ --policy.path=lerobot/smolvla_vlabench \ --env.type=vlabench \ --env.task=primitive \ --eval.batch_size=1 \ --eval.n_episodes=10 \ --eval.use_async_envs=false \ --policy.device=cuda \ --env.max_parallel_tasks=1 \ '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}' ``` Or both suites: ```bash lerobot-eval \ --policy.path=lerobot/smolvla_vlabench \ --env.type=vlabench \ --env.task=primitive,composite \ --eval.batch_size=1 \ --eval.n_episodes=10 \ --eval.use_async_envs=false \ --policy.device=cuda \ --env.max_parallel_tasks=1 \ '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}' ``` ### Recommended evaluation episodes **10 episodes per task** for reproducible benchmarking (210 total for the full primitive suite, 220 for composite). Matches the protocol in the VLABench paper. ## Policy inputs and outputs **Observations:** - `observation.state` — 7-dim end-effector state (position xyz + Euler xyz + gripper) - `observation.images.image` — front camera, 480×480 HWC uint8 - `observation.images.second_image` — second camera, 480×480 HWC uint8 - `observation.images.wrist_image` — wrist camera, 480×480 HWC uint8 **Actions:** - Continuous control in `Box(-1, 1, shape=(7,))` — 3D position + 3D Euler orientation + 1D gripper. ## Training ### Datasets Pre-collected VLABench datasets in LeRobot format on the Hub: - [`VLABench/vlabench_primitive_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_primitive_ft_lerobot_video) — 5,000 episodes, 128 tasks, 480×480 images. - [`VLABench/vlabench_composite_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_composite_ft_lerobot_video) — 5,977 episodes, 167 tasks, 224×224 images. ### Example training command Fine-tune a SmolVLA base on the primitive suite: ```bash lerobot-train \ --policy.type=smolvla \ --policy.repo_id=${HF_USER}/smolvla_vlabench_primitive \ --policy.load_vlm_weights=true \ --policy.push_to_hub=true \ --dataset.repo_id=VLABench/vlabench_primitive_ft_lerobot_video \ --env.type=vlabench \ --env.task=select_fruit \ --output_dir=./outputs/smolvla_vlabench_primitive \ --steps=100000 \ --batch_size=4 \ --env_eval_freq=5000 \ --eval.batch_size=1 \ --eval.n_episodes=1 \ --save_freq=10000 ``` ## Reproducing published results The released checkpoint [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench) was trained on the primitive-suite dataset above and is evaluated with the [Single-task](#single-task-evaluation-recommended-for-quick-iteration) / [Suite-wide](#suite-wide-evaluation) commands. CI runs a 10-primitive-task smoke eval (one episode each) on every PR touching the benchmark.