視界を取り戻せ：ロボットマニピュレーションにおける遮蔽回復のための物理的アクティブビジョンベンチマーク
Recovering the View: Benchmarking Physical Active Vision for Occlusion Recovery in Robotic Manipulation
両腕ロボットの遮蔽回復を評価するベンチマークBAVO-Benchを提案し、未来予測を活用したアクティブビジョン方策A-FARで遮蔽への頑健性を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Kaijun Luo, Yudi Huang, Qijun Zhong, Xinshuai Song, Yang Liu, Liang Lin
分類: cs.RO
原文アブストラクト
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.