異種マルチチームシステムにおける利他的協調の学習
Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
ロボットを移譲可能な資源とみなし、生態学のハミルトン則に基づく利他的意思決定で異種チーム間の協調を実現する枠組みを提案。グラフニューラルネットワークで割り当て問題を近似し、消防シナリオで有効性を検証した。
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: Riwa Karam, Ruoyu Lin, Brooks A. Butler, Magnus Egerstedt
分類: cs.RO, cs.AI, cs.MA, cs.SY, eess.SY
原文アブストラクト
This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting allocation problem is combinatorial and is shown to be NP-hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot-level transfer decisions and next robot-to-team assignments. The proposed approach is validated in a firefighting scenario through simulations and experiments, demonstrating that the learned policy achieves near-optimal performance while scaling to larger systems.
