Implicit Coordination with Shared Belief: A Heterogeneous Robot Soccer Team Case Study

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A striking aspect of human coordination is that we achieve it with little or no communication. We achieve this implicit coordination by taking the perspective of others and inferring their intentions. In contrast, robots usually coordinate explicitly through the extensive communication of utilities or intentions. In this paper we present a method that combines both approaches: implicit coordination with shared belief. In this approach, robots first communicate their beliefs about the world state to each other, using a CORBA-based communication module. They then use learned utility prediction models to predict the utility of each robot locally. Based on these utilities, an action is chosen. Within a heterogeneous soccer team, with robots from both the Munich and Ulm research groups, we apply implicit coordination with shared belief to a typical task from robotic soccer: regaining ball possession. An empirical evaluation demonstrates that the redundancy of implicit coordination with shared belief leads to robustness against communication failure and state estimation inaccuracy.