アリに学ぶ群れ分離のための壁形成戦略：有限状態機械に基づく強化学習アプローチ
Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines
アリの壁形成行動に着想を得て、異種ロボット群が並行タスクを実行しながら空間的に分離を保つための2つの分散制御器（FSMベースとDQN統合型）を開発し、シミュレーションで混合を40〜50%削減した。
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？
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著者: Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu, Spring Berman, Theodore P. Pavlic
分類: cs.RO
原文アブストラクト
In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence.
