| title | Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization |
|---|---|
| authors | Yifan Li, Jiaxu Wang, Dongming Wu, Yicheng Jiang, Ryan Ji, Xiangyu Yue, Yanwei Fu |
| date | 2026-10-04 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.05230 |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| primer | Vision-language-action models |
| abstract | Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed output budget. This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be learned implicitly, and forces a tradeoff between long-horizon coverage and the local precision required for contact-rich manipulation. To address these limitations, we introduce Vela, a vision-language-action foundation model that represents future robot behavior as continuous trajectories. Vela combines a compact spline-based action representation with motion-dependent temporal support and a shared action interface for heterogeneous embodiments, allowing a fixed output budget to adapt its temporal resolution across motions. We pretrain Vela on large-scale multi-embodiment robot data and evaluate it on LIBERO-X, EBench, and two real-world long-horizon tasks, egg-cake cooking and potato shredding, obtaining promising results across simulation and physical manipulation. These results highlight the potential of continuous action representations as a foundation for future embodied foundation models. Project page and more results: https://clementine24.github.io/Vela/ . |
cite
@misc{li2026vela,
title={Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization},
author={Yifan Li and Jiaxu Wang and Dongming Wu and Yicheng Jiang and Ryan Ji and Xiangyu Yue and Yanwei Fu},
year={2026},
eprint={2610.05230},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.05230}
}