mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-26 19:26:16 +00:00
Pepijn
1747767111
VLABench is not on PyPI — its only distribution is the OpenMOSS/VLABench GitHub repo (package name `VLABench`, no PyPI release) — so the `vlabench>=0.1.0` spec in the `[vlabench]` extra was flatly unsatisfiable. Every `uv sync` (even with unrelated extras like `--extra test`) validates the full lockfile graph and tripped on this, breaking the fast-test CI step. - Remove `vlabench = [...]` from pyproject.toml (left an explanatory comment in its place) and drop the `lerobot[vlabench]` entry from the `all` extra. - Update docs/source/vlabench.mdx to document the manual `git clone` + editable install flow for VLABench, rrt-algorithms and the MuJoCo / dm_control pins — same pattern as robocasa. - Docker image already does this via explicit git clones, so no CI image change is needed. Made-with: Cursor
157 lines
6.4 KiB
Plaintext
157 lines
6.4 KiB
Plaintext
# VLABench
|
||
|
||
[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.
|
||
|
||
- 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)
|
||
|
||
## 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
|
||
```
|
||
|
||
<Tip>
|
||
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)
|
||
```
|
||
|
||
</Tip>
|
||
|
||
## Evaluation
|
||
|
||
### 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
|
||
```
|
||
|
||
### 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
|
||
```
|
||
|
||
### 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 \
|
||
--env.max_parallel_tasks=1
|
||
```
|
||
|
||
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 \
|
||
--env.max_parallel_tasks=1
|
||
```
|
||
|
||
### 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 \
|
||
--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.
|