AI RESEARCH
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
arXiv CS.LG
•
ArXi:2604.02615v1 Announce Type: new Graph neural networks (GNNs) are a well-regarded tool for learned control of networked dynamical systems due to their ability to be deployed in a distributed manner. However, current distributed GNN architectures assume that all nodes in the network collect geometric observations in compatible bases, which limits the usefulness of such controllers in GPS-denied and compass-denied environments. This paper presents a GNN parametrization that is globally invariant to choice of local basis.