  This paper addresses the Multi-Robot Active Information Acquisition (AIA)
problem, where a team of mobile robots, communicating through an underlying
graph, estimates a hidden state expressing a phenomenon of interest.
Applications like target tracking, coverage and SLAM can be expressed in this
framework. Existing approaches, though, are either not scalable, unable to
handle dynamic phenomena or not robust to changes in the communication graph.
To counter these shortcomings, we propose an Information-aware Graph Block
Network (I-GBNet), an AIA adaptation of Graph Neural Networks, that aggregates
information over the graph representation and provides sequential-decision
making in a distributed manner. The I-GBNet, trained via imitation learning
with a centralized sampling-based expert solver, exhibits permutation
equivariance and time invariance, while harnessing the superior scalability,
robustness and generalizability to previously unseen environments and robot
configurations. Experiments on significantly larger graphs and dimensionality
of the hidden state and more complex environments than those seen in training
validate the properties of the proposed architecture and its efficacy in the
application of localization and tracking of dynamic targets.
