| title | From Pixels to Actions - Towards Native Vision-Language-Action Policies |
|---|---|
| authors | Weichen Fan, Haiwen Diao, Penghao Wu, Siyuan Hu, Ziqi Huang, Tianqi Liu, Mingze Sun, Ziyi Wang, Zhongang Cai, Hanming Deng, Quan Wang, Lei Yang, Lewei Lu, Fangzhou Hong, Zhaoxi Chen, Dahua Lin, Ziwei Liu |
| date | 2026-11-04 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.06918 |
| affiliation | MMLab@NTU |
| upvotes | 56 (Hugging Face Daily Papers, 2026-11-06) |
| links | abs pdf hf |
| cite | |
| primer | Vision-language-action models |
| abstract | Vision-language-action models are typically built on a simple premise: perception is first compressed by visual encoders, and action reasoning begins only afterwards. Yet manipulation often hinges on precisely the visual signals most vulnerable to this abstraction, e.g., local geometry, boundaries, depth, and subtle state changes. This raises a fundamental question: should action policies inherit a fixed visual endpoint, or directly access evolving visual computation? We introduce NEO-VLA, a structurally encoder-free vision-language-action framework. Images are tokenized by lightweight patch convolutional layers and evolve jointly with language throughout the backbone, allowing a flow-matching action expert to draw from instruction-conditioned visual representations at different depths. This simple change reveals a consistent pattern. Native architectures outperform encoder-based counterparts under controlled comparisons; full fine-tuning reduces but does not erase the gap, and additional scale alone cannot recover it. More strikingly, layer-wise probing shows that the action-critical signals often peak well before the final visual representation. Across diverse manipulation settings and distribution shifts, NEO-VLA consequently achieves stronger and more robust control. Together, these results suggest that embodied control benefits from access to visual representations across the computational hierarchy, rather than a fixed encoder output. |
bibtex
@misc{fan2026from,
title={From Pixels to Actions - Towards Native Vision-Language-Action Policies},
author={Weichen Fan and Haiwen Diao and Penghao Wu and Siyuan Hu and Ziqi Huang and Tianqi Liu and Mingze Sun and Ziyi Wang and Zhongang Cai and Hanming Deng and Quan Wang and Lei Yang and Lewei Lu and Fangzhou Hong and Zhaoxi Chen and Dahua Lin and Ziwei Liu},
year={2026},
eprint={2611.06918},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.06918}
}