GAMBIT: 連続マルチロボット軌道計画の学習
GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
マルチロボットの軌道実行において、模倣学習と強化学習を組み合わせて協調的な動作プリミティブ選択を学習し、衝突回避を保証する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: Rishabh Jain, Akmaral Moldagalieva, Lorenzo Magnino, Michael Amir, Keisuke Okumura, Ajay Shankar, Wolfgang Hönig, Amanda Prorok
分類: cs.RO, cs.AI, cs.LG, cs.MA
原文アブストラクト
GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously, in multi-robot coordination, individual robots may need to forgo locally reward-maximising behaviours to improve overall team performance. Such self-sacrificial behaviours are difficult to capture with manually designed heuristics, particularly in dense, interaction-rich environments. Focusing on double-integrator continuous dynamics, this work studies how to learn such coordinated heuristics over motion primitives for multi-robot trajectory execution. Our framework, GAMBIT, first learns coordinated motion-primitive selection through imitation learning and subsequently fine-tunes the policy through reinforcement learning. We further introduce a safeguarded rollout mechanism with backup trajectories that guarantees collision-free execution at all times. Experiments demonstrate that GAMBIT substantially outperforms a range of baselines, including centralised motion planners and decentralised reactive planners, while exhibiting strong scalability. In particular, it coordinates over a thousand robots with planning latency below a few hundred milliseconds in continuous domains.