視認性を考慮したタスク・動作計画とタスク中心の知覚
Visibility-Aware Task and Motion Planning with Task-Centric Perception
部分的にしか観測できない物体配置タスクに対し、センシング動作を計画に組み込み、物体メッシュ仮説と占有マップを永続メモリで統合する視認性考慮型TAMPを提案し、実機の卓上整理で有効性を示した。
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？
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6. 次に読むべき論文は？
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著者: Sahit Chintalapudi, Xiaoyan Wang, Aryan Zoroufi, Aditya Agarwal, Nancy Kanwisher, Tomás Lozano-Pérez, Leslie Pack Kaelbling
分類: cs.RO
原文アブストラクト
Long-horizon manipulation under uncertainty remains a challenging problem in robotics. We present \ourmethod{}, an algorithm for Visibility-Aware Task and Motion Planning. \ourmethod{} extends existing deterministic task and motion planning solvers to partially observable object rearrangement tasks, while still being able to take advantage of the efficiency and compositional generalization over long horizons of the base solver. This extension interleaves sensing actions into a plan skeleton that assumed full observability, and uses a novel persistent object-based memory to fuse observations into object mesh hypotheses and an observed occupancy map. We evaluate \ourmethod{} on real-world tabletop rearrangement tasks which feature heavy occlusion and partial visibility and demonstrate that it outperforms baselines that do not perform active perception or have persistent memory.