グラフニューラルネットワークによるロボット群の分散制御学習
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
訓練時に集中制御のポリシーを模倣し、テスト時には局所情報のみで動く分散コントローラをグラフニューラルネットで学習。群れ行動で通信半径や速度変化の影響を検証した。
著者: Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, Alejandro Ribeiro
分類: cs.RO
原文アブストラクト
We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to the problem of flocking to demonstrate performance on communication graphs that change as the robots move. We examine how a decreasing communication radius and faster velocities increase the value of multi-hop information.
