  In decentralized multi-robot navigation, ensuring safe and efficient movement
with limited environmental awareness remains a challenge. While robots
traditionally navigate based on local observations, this approach falters in
complex environments. A possible solution is to enhance understanding of the
world through inter-agent communication, but mere information broadcasting
falls short in efficiency. In this work, we address this problem by
simultaneously learning decentralized multi-robot collision avoidance and
selective inter-agent communication. We use a multi-head self-attention
mechanism that encodes observable information from neighboring robots into a
concise and fixed-length observation vector, thereby handling varying numbers
of neighbors. Our method focuses on improving navigation performance through
selective communication. We cast the communication selection as a link
prediction problem, where the network determines the necessity of establishing
a communication link with a specific neighbor based on the observable state
information. The communicated information enhances the neighbor's observation
and aids in selecting an appropriate navigation plan. By training the network
end-to-end, we concurrently learn the optimal weights for the observation
encoder, communication selection, and navigation components. We showcase the
benefits of our approach by achieving safe and efficient navigation among
multiple robots, even in dense and challenging environments. Comparative
evaluations against various learning-based and model-based baselines
demonstrate our superior navigation performance, resulting in an impressive
improvement of up to 24% in success rate within complex evaluation scenarios.
