マルチロボットネットワークにおけるノード軌道からのグラフ同定学習
Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks
ノードの状態軌道からネットワークの相互作用グラフを推定するため、自己注意エンコーダと強凸最適化を組み合わせ、未知の構成のネットワークにも汎化できる学習手法を提案した。
著者: Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano, Carlos Sagues
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
The graph identification problem consists of discovering the interactions among nodes in a network given their state/feature trajectories. This problem is challenging because the behavior of a node is coupled to all the other nodes by the unknown interaction model. Besides, high-dimensional and nonlinear state trajectories make it difficult to identify if two nodes are connected. Current solutions rely on prior knowledge of the graph topology and the dynamic behavior of the nodes, and hence, have poor generalization to other network configurations. To address these issues, we propose a novel learning-based approach that combines (i) a strongly convex program that efficiently uncovers graph topologies with global convergence guarantees and (ii) a self-attention encoder that learns to embed the original state trajectories into a feature space and predicts appropriate regularizers for the optimization program. In contrast to other works, our approach can identify the graph topology of unseen networks with new configurations in terms of number of nodes, connectivity or state trajectories. We demonstrate the effectiveness of our approach in identifying graphs in multi-robot formation and flocking tasks.
