Adaptive spatio-temporal organization in groups of robots

This paper presents experiments, in simulation, with a group of robots that improve their performance on a straightforward transportation task by using reinforcement learning to associate input states with a set of abstract behaviors. We show that the improvement in performance is a result of the group adapting its spatio-temporal organization to the given environment. Spatio-temporal adaptation is a general form of adaptation in that it can improve performance over a range of different tasks and environments. Hence it increases the general applicability and autonomy of robotic systems. Lastly, we present two communication strategies that improve this ability to adapt by generally improving learning rates for cooperative robots in highly dynamic domains.