接触を許容する安全なロボット操作のためのスケーラブルなバリア関数フレームワーク
Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter
密集環境での接触を安全とみなす新たなバリア関数を学習し、任意の物体配置に合成可能でタスク転移も容易な安全フィルタを実現した。
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: Haixin Jin, Nikhil Uday Shinde, Soofiyan Atar, Hongzhan Yu, Dylan Hirsch, Sicun Gao, Michael C. Yip, Sylvia Herbert
分類: cs.RO
原文アブストラクト
Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting robots to collision avoidance and limiting their ability to function in dense, everyday settings. As the number of objects grows, model-based approaches for safe manipulation become computationally intractable; meanwhile, learned methods typically tie safety to the task at hand, making them hard to transfer to new tasks without retraining. In this work we introduce Dense Contact Barrier Functions(DCBF). Our approach bypasses the computational complexity of explicitly modeling multi-object dynamics by instead learning a composable, object-centric function that implicitly captures the safety constraints arising from physical interactions. Trained offline on interactions with a few objects, the learned DCBFcomposes across arbitrary object sets at runtime, producing a single global safety filter that scales linearly and transfers across tasks without retraining. We validate our approach through simulated experiments in dense clutter, demonstrating its ability to enable collision-free navigation and safe, contact-rich interaction in suitable settings.
