ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware
Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enabling vision-language-action (VLA) models to reason across changing viewpoints and actively acquire informative observations remains challenging. We present ActiveScale, a framework that advances active perception through coordinated model, data, and hardware designs. Our model augments a VLA with historical video observations and explicit camera-pose supervision, using per-frame pose tokens and a lightweight prediction head to associate observations across viewpoints and support a coherent understanding of the scene. To learn from the camera mo...
| Authors | Shuai Zhou, Kaisheng Pang, Wenxuan Song et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 September 2026 |
| Last updated at source | 19 September 2026 |
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