SurgVIL: オープンソース手術動画による手術ロボット模倣学習の大規模化
SurgVIL: Scaling Surgical Robot Imitation Learning with Open-source Surgical Videos
手術ロボットの模倣学習において、実組織の動画データから弱い教師信号として運動学を推定し、ファントムデータと組み合わせて学習することで、実組織や分布外環境への汎化性能を向上させるフレームワークを提案した。
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: Xinhao Chen, JuoTung Chen, Nigel Nelson, Antony Goldenberg, Jesse Haworth, Sean D. Huver, Axel Krieger
分類: cs.RO
原文アブストラクト
Learning-based surgical robot autonomy requires large-scale demonstrations with synchronized videos and robot actions, but such data are exceedingly rare in clinical or realistic tissue settings because robot kinematics are typically inaccessible outside controlled research systems. In contrast, phantom data collected on research platforms provide accurate action labels but lack the visual diversity of real tissue. We propose SurgVIL, a framework for scaling surgical robot imitation learning using open-source surgical videos. SurgVIL combines kinematically labeled phantom robot demonstrations with surgical videos from open-source datasets and online sources for policy learning. Since these videos lack robot motion labels, we estimate approximate kinematics as weak supervision. We evaluate SurgVIL on two da Vinci robot tasks: needle pick-up and cholecystectomy cutting. Across ACT, $π_0$, and GR00T-H backbones, adding surgical videos substantially improves generalization to real-tissue and out-of-distribution settings, suggesting a scalable path from phantom training toward generalizable surgical robot policies.