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We propose a method, based on empirical game theory, for a robot operating aspart of a team to choose its role within the team without explicitlycommunicating with team members, by leveraging its knowledge about the teamstructure. To do this, we formulate the role assignment problem as a dynamicgame, and borrow tools from empirical game-theoretic analysis to analyze suchgames. Based on this game-theoretic formulation, we propose a distributedcontroller for each robot to dynamically decide on the best role to take. Wedemonstrate our method in simulations of a collaborative planar manipulationscenario in which each agent chooses from a set of feedback control policies ateach instant. The agents can effectively collaborate without communication tomanipulate the object while also avoiding collisions using our method.

Decentralized Role Assignment in Multi-Agent Teams via Empirical Game-Theoretic Analysis