| title | UniWAM: Unified World-Action Model |
|---|---|
| authors | Jiayi Chen, Wenxuan Song, Jingbo Wang, Shuai Zhou, Xicheng Gong, Zehua Fan, Ziyang Zhou, Junwu E, Haodong Yan, Fuhao Li, Qize Yu, Xu Huang, Pengwei Wang, Wen Chen, Shunbo Zhou, Haoang Li |
| date | 2026-10-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.02054 |
| affiliation | HKUSTGZ |
| upvotes | 77 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | Vision-language-action models |
| abstract | Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-action models inherit spatiotemporal priors from video generation models, yet remain limited in semantic understanding and reasoning under distribution shifts. We introduce UniWAM, a unified architecture that integrates a physical reasoner, a world generator, and an action predictor to jointly learn semantic understanding of the physical world, visual generation, and action prediction. To ensure the quality of the training data, we developed a rigorous data cleaning and annotation pipeline for both human egocentric data and robot data. To adapt the vision-language component to embodied tasks while preserving its inherited language capabilities, we represent low-level actions in natural language and introduce a pre-training recipe that assigns complementary supervision from visual question answering (VQA) data, human egocentric data, and robot demonstrations to the appropriate model components. During post-training, future visual noise augmentation reduces reliance on precise future predictions, while history-conditioned flow matching uses encoded action history to initialize action generation. Together, these designs significantly reduce denoising steps while maintaining performance. UniWAM achieves state-of-the-art (SOTA) performance across multiple evaluations, including in-distribution performance, robustness, generalization, instruction following, and long-horizon task execution. Furthermore, we uncover a log-linear scaling law of unified human-robot co-training, demonstrating the effectiveness of large-scale pre-training on a mixture of human and robot data. |
cite
@misc{chen2026uniwam,
title={UniWAM: Unified World-Action Model},
author={Jiayi Chen and Wenxuan Song and Jingbo Wang and Shuai Zhou and Xicheng Gong and Zehua Fan and Ziyang Zhou and Junwu E and Haodong Yan and Fuhao Li and Qize Yu and Xu Huang and Pengwei Wang and Wen Chen and Shunbo Zhou and Haoang Li},
year={2026},
eprint={2610.02054},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.02054}
}