Graph Neural Networks for Learning Robot Team Coordination

Understand

This paper shows how Graph Neural Networks can be used for learning distributed coordination mechanisms in connected teams of robots.

• We capture the relational aspect of robot coordination by modeling the robot team as a graph, where each robot is a node, and edges represent communication links.
• During training, robots learn how to pass messages and update internal states, so that a target behavior is reached.
• As a proxy for more complex problems, this short paper considers the problem where each robot must locally estimate the algebraic connectivity of the team's network topology.