  We propose a method, based on empirical game theory, for a robot operating as
part of a team to choose its role within the team without explicitly
communicating with team members, by leveraging its knowledge about the team
structure. To do this, we formulate the role assignment problem as a dynamic
game, and borrow tools from empirical game-theoretic analysis to analyze such
games. Based on this game-theoretic formulation, we propose a distributed
controller for each robot to dynamically decide on the best role to take. We
demonstrate our method in simulations of a collaborative planar manipulation
scenario in which each agent chooses from a set of feedback control policies at
each instant. The agents can effectively collaborate without communication to
manipulate the object while also avoiding collisions using our method.
