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

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: this https URL .

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.05230 [cs.RO]

(or arXiv:2610.05230v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05230 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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