EgoReveal: 一人称視点のデモンストレーションから部分遮蔽下での能動視覚と物体操作を学習
EgoReveal: Learning Active Vision and Object Manipulation under Partial Occlusions from Egocentric Demonstrations
一人称視点の人間デモから、対象が部分的に隠れている状況で視点を動かして見えるようにしつつ操作する方策を学ぶフレームワークを提案。
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: Hiromu Kurihara, Kazutoshi Tanaka, Masashi Hatano, Tadashi Kozuno, Hideo Saito, Mariko Isogawa
分類: cs.RO
原文アブストラクト
Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and labor-intensive teleoperation, egocentric demonstrations are easy to collect, enabling scalable data collection. Manipulated objects are often partially occluded in everyday environments, yet prior egocentric demonstration-based methods that require no robots or specialized teaching devices do not explicitly address occlusions. To fill this gap, we propose EgoReveal, a framework that learns viewpoint-repositioning together with manipulation actions from egocentric human demonstrations to reposition the viewpoint and obtain target visibility under partial occlusions. EgoReveal instantiates this as two imitation policies: one predicts the future camera-arm pose sequence from the current view and camera-arm pose, and the other predicts the future control commands from object keypoints and a history of control commands. EgoReveal switches between the two based on target visibility. In experiments where the manipulated object is partially occluded initially, EgoReveal repositions the camera to obtain visibility and completes the task more reliably than fixed-camera baselines.