When2com: Multi-Agent Perception via Communication Graph Grouping
This addresses the need for distributed and bandwidth-efficient multi-agent perception systems, which is incremental as it builds on existing collaborative perception methods.
The paper tackles the problem of multi-agent collaborative perception by proposing a framework that learns to construct communication groups and decide when to communicate, significantly reducing communication bandwidth while maintaining superior performance.
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.