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Pepijn
33864a32c2
Rework docs/source/vlabench.mdx to follow the standard benchmark doc structure: intro + links + available tasks (with cleaner counts and comma-list shortcuts) + installation + eval + recommended episodes + policy I/O + training + reproducing results. - Point everything at lerobot/smolvla_vlabench (the released checkpoint) rather than a generic "your-policy-id" placeholder. - Document the three valid `--env.task` forms (single, comma list, suite shortcut). - Drop the "Evaluation tracks" table — it reflected the upstream paper rather than what the LeRobot env exposes, and was confusing. - Add an explicit "Reproducing published results" section pointing at the CI smoke eval. Made-with: Cursor
157 lines
6.0 KiB
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157 lines
6.0 KiB
Plaintext
# VLABench
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[VLABench](https://github.com/OpenMOSS/VLABench) is a large-scale benchmark for **language-conditioned robotic manipulation with long-horizon reasoning**. It 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.
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- Paper: [VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning](https://arxiv.org/abs/2412.18194)
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- GitHub: [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench)
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- Project website: [vlabench.github.io](https://vlabench.github.io)
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- Pretrained policy: [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench)
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## Available tasks
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VLABench ships two task suites covering **43 task categories** in LeRobot's `--env.task` surface:
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| Suite | CLI name | Tasks | Description |
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| --------- | ----------- | ----- | ---------------------------------------------------------------- |
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| Primitive | `primitive` | 21 | Single / few-skill combinations (select, insert, physics QA) |
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| Composite | `composite` | 22 | Multi-step reasoning and long-horizon planning (cook, rearrange) |
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**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`).
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**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`.
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`--env.task` accepts three forms:
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- a single task name (`select_fruit`)
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- a comma-separated list (`select_fruit,heat_food`)
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- a suite shortcut (`primitive`, `composite`, or `primitive,composite`)
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## Installation
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After following the standard LeRobot installation instructions:
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```bash
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pip install -e ".[vlabench]"
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```
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VLABench also ships simulation assets separately. Clone the repo and download them:
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```bash
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git clone https://github.com/OpenMOSS/VLABench.git
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cd VLABench
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python scripts/download_assets.py
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```
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<Tip>
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VLABench requires Linux (`sys_platform == 'linux'`) and Python 3.10+. Set the MuJoCo rendering backend before running:
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```bash
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export MUJOCO_GL=egl # for headless servers (HPC, cloud)
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```
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</Tip>
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## Evaluation
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### Single-task evaluation (recommended for quick iteration)
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```bash
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lerobot-eval \
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--policy.path=lerobot/smolvla_vlabench \
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--env.type=vlabench \
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--env.task=select_fruit \
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--eval.batch_size=1 \
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--eval.n_episodes=10
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```
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### Multi-task evaluation
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Pass a comma-separated list of tasks:
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```bash
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lerobot-eval \
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--policy.path=lerobot/smolvla_vlabench \
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--env.type=vlabench \
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--env.task=select_fruit,select_toy,add_condiment,heat_food \
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--eval.batch_size=1 \
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--eval.n_episodes=10
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```
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### Suite-wide evaluation
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Run an entire suite (all 21 primitives or all 22 composites):
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```bash
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lerobot-eval \
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--policy.path=lerobot/smolvla_vlabench \
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--env.type=vlabench \
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--env.task=primitive \
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--eval.batch_size=1 \
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--eval.n_episodes=10 \
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--env.max_parallel_tasks=1
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```
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Or both suites:
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```bash
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lerobot-eval \
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--policy.path=lerobot/smolvla_vlabench \
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--env.type=vlabench \
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--env.task=primitive,composite \
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--eval.batch_size=1 \
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--eval.n_episodes=10 \
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--env.max_parallel_tasks=1
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```
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### Recommended evaluation episodes
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**10 episodes per task** for reproducible benchmarking (210 total for the full primitive suite, 220 for composite). Matches the protocol in the VLABench paper.
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## Policy inputs and outputs
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**Observations:**
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- `observation.state` — 7-dim end-effector state (position xyz + Euler xyz + gripper)
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- `observation.images.image` — front camera, 480×480 HWC uint8
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- `observation.images.second_image` — second camera, 480×480 HWC uint8
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- `observation.images.wrist_image` — wrist camera, 480×480 HWC uint8
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**Actions:**
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- Continuous control in `Box(-1, 1, shape=(7,))` — 3D position + 3D Euler orientation + 1D gripper.
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## Training
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### Datasets
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Pre-collected VLABench datasets in LeRobot format on the Hub:
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- [`VLABench/vlabench_primitive_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_primitive_ft_lerobot_video) — 5,000 episodes, 128 tasks, 480×480 images.
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- [`VLABench/vlabench_composite_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_composite_ft_lerobot_video) — 5,977 episodes, 167 tasks, 224×224 images.
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### Example training command
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Fine-tune a SmolVLA base on the primitive suite:
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```bash
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lerobot-train \
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--policy.type=smolvla \
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--policy.repo_id=${HF_USER}/smolvla_vlabench_primitive \
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--policy.load_vlm_weights=true \
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--policy.push_to_hub=true \
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--dataset.repo_id=VLABench/vlabench_primitive_ft_lerobot_video \
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--env.type=vlabench \
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--env.task=select_fruit \
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--output_dir=./outputs/smolvla_vlabench_primitive \
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--steps=100000 \
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--batch_size=4 \
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--eval_freq=5000 \
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--eval.batch_size=1 \
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--eval.n_episodes=1 \
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--save_freq=10000
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```
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## Reproducing published results
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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.
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