Large-scale, decentralized multi-robot coordination through graph neural networks
For long-term operation in large-scale environments, we need scalable and decentralized algorithms. However, with local communications, these decentralized algorithms typically perform worse than their centralized counterparts. Recently, I have been exploring the use of graph neural networks (GNNs) as a tool for automatically synthesizing decentralized planning strategies which are trained to imitate centralized experts. To this end, I developed a GNN-based imitation learning framework that learns decentralized decision-making for the robots from a centralized expert in small-scale scenarios and generalizes well the learned policies to larger-scale scenarios, e.g., larger environments and larger networks of robots.
Preprint: Graph neural networks for decentralized multi-robot submodular action selection.