README.md

---
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}
}
```