Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization

Yifan Li, Jiaxu Wang, Dongming Wu, Yicheng Jiang, Ryan Ji, Xiangyu Yue, Yanwei Fu

Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization: 5 upvotes on Hugging Face Daily Papers, #81 of 89 papers on 2026-10-15. Day-by-day upvote history.

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/ .

Paper page on Hugging Face · arXiv

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