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pretty_name: VA-Bench
license: mit
tags:
- robotics
- embodied-ai
- active-perception
- robot-manipulation
- 3d
- benchmark
- simulation
configs:
- config_name: tasks
data_files:
- split: train
path: data/tasks.jsonl
- config_name: assets
data_files:
- split: train
path: data/assets.jsonl
---
# VA-Bench
VA-Bench evaluates embodied spatial intelligence through visual demonstrations,
active perception, and metric robot control. It contains 14 physics-simulated
manipulation task families: 11 single-arm tasks and three dual-arm tasks.
- Paper: [VA-Bench](https://arxiv.org/abs/2609.19554)
- Code and benchmark: [github.com/zhangzhongbo2213/VABench](https://github.com/zhangzhongbo2213/VABench)
- Upstream simulator: [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin)
- Upstream assets: [RoboTwin2.0](https://huggingface.co/datasets/TianxingChen/RoboTwin2.0)
## Contents
This repository distributes the versioned simulator assets needed by the 14
main VA-Bench tasks. The archive contains the ALOHA-AgileX robot description,
planning and collision configurations, and every object variant selected by
the `vabench_eval` and `demo_clean` task configurations.
| File | Purpose |
|:--|:--|
| `va-bench-main-assets-v1.tar.gz` | Complete main-task asset archive |
| `va-bench-main-assets-v1.tar.gz.sha256` | Archive checksum |
| `ASSET_MANIFEST.json` | Per-file size and SHA-256 manifest |
| `VALIDATION.json` | Release validation record |
| `data/tasks.jsonl` | Structured index of the 14 task families |
| `data/assets.jsonl` | Structured index of the 189 bundled asset files |
The archive expands to an `assets/` directory. It does not contain the full
RoboTwin asset collection; randomized backgrounds and clutter require the
upstream assets.
## Download the simulator assets
The binary archive is a simulator resource bundle rather than a table of
training examples. Download it with `huggingface_hub`:
```python
from huggingface_hub import hf_hub_download
archive = hf_hub_download(
repo_id="zhangzhongbo2213/VA-Bench",
repo_type="dataset",
filename="va-bench-main-assets-v1.tar.gz",
revision="assets-v1",
)
print(archive)
```
Extract it from the root of a VA-Bench checkout:
```bash
tar --keep-old-files -xzf /path/to/va-bench-main-assets-v1.tar.gz -C /path/to/VABench
python /path/to/VABench/script/check_setup.py
```
The expected archive SHA-256 is:
```text
1a264fd776997b3dc32e4ba5d2334a00d117790b80afa851ac2bdbc31ce5f4bf
```
## Browse the metadata
The Dataset Viewer and `datasets` library expose metadata indexes; they do not
materialize the simulator asset tree:
```python
from datasets import load_dataset
tasks = load_dataset(
"zhangzhongbo2213/VA-Bench", "tasks", split="train"
)
assets = load_dataset(
"zhangzhongbo2213/VA-Bench", "assets", split="train"
)
```
Each task row records the active arm, action horizon, demonstration seed, and
20 physically verified evaluation seeds. Each asset row records its archive
path, byte size, and SHA-256 digest.
## Validation
The release was validated from an independently extracted bundle. All 14 task
environments reset successfully, rendered 1280x960 RGB observations, executed a
camera action, and wrote replay videos. All 18 selected object variants loaded,
rendered, and stepped. See `VALIDATION.json` for the complete record. These are
environment checks, not model-performance evaluations.
## License and provenance
The selected assets originate from RoboTwin2.0 and are distributed under the
MIT License. The upstream copyright and license are retained in `LICENSE`, and
the source and modification notice is retained in `NOTICE.md`.
Geometry and textures are unchanged. Only two cuRobo configuration files were
modified to replace machine-specific asset paths with portable relative paths.
## Citation
```bibtex
@article{zhang2026vabench,
title = {VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control},
author = {Zhang, Zhongbo and Jin, Jiayi and Wang, Yifan and Zhang, Zaibin and Diao, Haiwen and Wang, Lijun and Lu, Huchuan},
journal = {arXiv preprint arXiv:2609.19554},
year = {2026}
}
```
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