SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoint-invariant execution. We propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Our approach decouples camera and manipulation actions rather than placing them in a shared action space, and follows a bottom-up training strategy: we first train semantic camera control on a large-scale dataset, then jointly optimize both action types using hybrid data. To support this framework, we introduce ActiveViewPose-200K, a dataset of 200k image-language-camera movement ...
| Authors | Mengzhen Liu, Enshen Zhou, Cheng Chi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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