RotVLA: Rotational Latent Action for Vision-Language-Action Model
Latent Action Models (LAMs) have emerged as an effective paradigm for handling heterogeneous datasets during Vision-Language-Action (VLA) model pretraining, offering a unified action space across embodiments. However, existing LAMs often rely on discrete quantization encode and decode pipelines, which can lead to trivial frame reconstruction behavior, limited representational capacity, and a lack of physically meaningful structure. We introduce RotVLA, a VLA framework built on a continuous rotational latent action representation. Latent actions are modeled as elements of SO(n), providing continuity, compositionality, and structured geometry aligned with real-world action dynamics. A tripl...
| Authors | Qiwei Li, Xicheng Gong, Xinghang Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 May 2026 |
| Last updated at source | 24 September 2026 |
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