Recovering the View: Benchmarking Physical Active Vision for Occlusion Recovery in Robotic Manipulation

Abstract:Physical active vision allows robots to change their viewpoint when task-relevant observations become unreliable, yet existing manipulation benchmarks provide limited support for studying how policies recover from occlusion during execution. We introduce BAVO-Bench (Bimanual Active Vision under Occlusion), a bimanual active-vision benchmark that systematically controls external visibility through Clean, Stage Occlusion, and Random-time Occlusion conditions, enabling evaluation of both manipulation performance and active visual recovery. Building on this setting, we present A-FAR (Active Future-Aware Recovery), an active-vision policy for joint viewpoint and manipulation control. A-FAR represents moving-camera observations in a unified robot-centric 3D frame and distills relational structure together with its future evolution from a pretrained 4D model, providing the policy with future-aware geometric guidance without requiring future observations at deployment. Experiments across multiple manipulation tasks show that A-FAR improves robustness to both structured and temporally shifted occlusions while maintaining strong performance under clean observations.

Robotics (cs.RO)

arXiv:2609.37292 [cs.RO]

(or arXiv:2609.37292v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.37292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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