  Coverage control is the problem of navigating a robot swarm to
collaboratively monitor features or a phenomenon of interest not known a
priori. The problem is challenging in decentralized settings with robots that
have limited communication and sensing capabilities. We propose a learnable
Perception-Action-Communication (LPAC) architecture for the problem, wherein a
convolution neural network (CNN) processes localized perception; a graph neural
network (GNN) facilitates robot communications; finally, a shallow multi-layer
perceptron (MLP) computes robot actions. The GNN enables collaboration in the
robot swarm by computing what information to communicate with nearby robots and
how to incorporate received information. Evaluations show that the LPAC models
-- trained using imitation learning -- outperform standard decentralized and
centralized coverage control algorithms. The learned policy generalizes to
environments different from the training dataset, transfers to larger
environments with more robots, and is robust to noisy position estimates. The
results indicate the suitability of LPAC architectures for decentralized
navigation in robot swarms to achieve collaborative behavior.
