---
license: apache-2.0
language:
  - en
base_model: robbyant/lingbot-va-base
tags:
  - robotics
  - world-action-model
  - event-aligned
  - robotwin
  - custom-code
---

# Event-Aligned — RoboTwin 2.0

Event-Aligned model for RoboTwin 2.0 bimanual manipulation, built on pretrained [LingBot-VA base](https://huggingface.co/robbyant/lingbot-va-base).

[Code][code-link] | [Models][models-link] | [Project page][project-page-link]

<p align="center">
  <a href="assets/teaser.png"><img src="assets/teaser.png" alt="Visual-action sampling and DOMINO success rates under a matched training sequence budget: Episode Uniform 29.17%, Event-Aligned 42.83%." width="800"></a>
  <br>
  <em>Event-aligned visual-action sampling (left) and DOMINO success rates under a matched training sequence budget (right).</em>
</p>

## Method

- **Event-aligned supervision:** preserve dense interaction evidence and sample transitions more sparsely, keeping training chunks within event boundaries.
- **Execution validity prediction:** identify valid action slots and skip redundant actions introduced by repeated-frame padding.

Inference uses observations and a task instruction. No event annotations are required during inference.

## Download

Download the RoboTwin model from the [model collection][models-link]:

```bash
python -m pip install -U huggingface_hub
hf download Xun49/Event-aligned-robotwin --local-dir ./model/event-aligned_robotwin
```

Download the complete repository, including `transformer/`, `vae/`, `text_encoder/`, and `tokenizer/`. See the [code repository][code-link] for setup, inference, and evaluation.

## Results reported in the paper

| Method | Clean SR (%) | Randomized SR (%) | Average SR (%) |
| --- | ---: | ---: | ---: |
| Event-Aligned | **93.02** | **91.58** | **92.30** |

Evaluation covers 50 RoboTwin 2.0 tasks under clean and randomized conditions, with 100 episodes per task for each condition.

## License and attribution

The model weights are released under the [Apache License 2.0](LICENSE.txt).

Built on the Robbyant Team's [LingBot-VA](https://github.com/Robbyant/lingbot-va/tree/7c6ffa9bfc4b83582cafc860fab4c82cc7deeeeb). The companion code is also Apache-2.0 licensed. Retain applicable upstream licenses and attribution. Please also credit *Causal World Modeling for Robot Control*.

## Citation

```bibtex
@misc{yang2026eventaligned,
  title         = {Event-Aligned Visual Action Reasoning for World Action Models},
  author        = {Yang, Xiaomeng and Wu, Yushu and Gao, Yi and Lei, Yuhao and Zhang, Xuan and Zhao, Pu and Wang, Yanzhi},
  year          = {2026},
  eprint        = {2610.09427},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2610.09427}
}
```

[code-link]: https://github.com/Xiaomeng-Yang/Event-aligned-WAM
[models-link]: https://huggingface.co/collections/Xun49/event-aligned
[project-page-link]: https://xiaomeng-yang.github.io/Event-aligned-WAM/