  Effective communication is key to successful, decentralized, multi-robot path
planning. Yet, it is far from obvious what information is crucial to the task
at hand, and how and when it must be shared among robots. To side-step these
issues and move beyond hand-crafted heuristics, we propose a combined model
that automatically synthesizes local communication and decision-making policies
for robots navigating in constrained workspaces. Our architecture is composed
of a convolutional neural network (CNN) that extracts adequate features from
local observations, and a graph neural network (GNN) that communicates these
features among robots. We train the model to imitate an expert algorithm, and
use the resulting model online in decentralized planning involving only local
communication and local observations. We evaluate our method in simulations {by
navigating teams of robots to their destinations in 2D} cluttered workspaces.
We measure the success rates and sum of costs over the planned paths. The
results show a performance close to that of our expert algorithm, demonstrating
the validity of our approach. In particular, we show our model's capability to
generalize to previously unseen cases (involving larger environments and larger
robot teams).
