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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/
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