  While significant advances have been made for single-agent perception, many
applications require multiple sensing agents and cross-agent communication due
to benefits such as coverage and robustness. It is therefore critical to
develop frameworks which support multi-agent collaborative perception in a
distributed and bandwidth-efficient manner. In this paper, we address the
collaborative perception problem, where one agent is required to perform a
perception task and can communicate and share information with other agents on
the same task. Specifically, we propose a communication framework by learning
both to construct communication groups and decide when to communicate. We
demonstrate the generalizability of our framework on two different perception
tasks and show that it significantly reduces communication bandwidth while
maintaining superior performance.
