MV-UMI：クロスエンボディメント学習のためのスケーラブルなマルチビューインターフェース
MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning
手持ちグリッパによるデータ収集に三人称視点を追加し、人間の実演とロボット展開のギャップを減らしてクロスエンボディメント性能を高めるMV-UMIを提案した。
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？
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: Omar Rayyan, John Abanes, Mahmoud Hafez, Anthony Tzes, Fares Abu-Dakka
分類: cs.RO, cs.AI
原文アブストラクト
Recent advances in imitation learning have shown great promise for developing robust robot manipulation policies from demonstrations. However, this promise is contingent on the availability of diverse, high-quality datasets, which are not only challenging and costly to collect but are often constrained to a specific robot embodiment. Portable handheld grippers have recently emerged as intuitive and scalable alternatives to traditional robotic teleoperation methods for data collection. However, their reliance solely on first-person view wrist-mounted cameras often creates limitations in capturing sufficient scene contexts. In this paper, we present MV-UMI (Multi-View Universal Manipulation Interface), a framework that integrates a third-person perspective with the egocentric camera to overcome this limitation. This integration mitigates domain shifts between human demonstration and robot deployment, preserving the cross-embodiment advantages of handheld data-collection devices. Our experimental results, including an ablation study, demonstrate that our MV-UMI framework improves performance in sub-tasks requiring broad scene understanding by approximately 47% across 3 tasks, confirming the effectiveness of our approach in expanding the range of feasible manipulation tasks that can be learned using handheld gripper systems, without compromising the cross-embodiment advantages inherent to such systems.
関連論文
- 身体性を超えた知能クロスエンボディメント学習
- カメラ座標系でロボット動作を統一するクロスエンボディメント学習
