Learning from history for behavior-based mobile robots in non-stationary conditions

Learning in the mobile robot domain is a very challenging task, especially in non-stationary conditions. The behavior-based approach has proven to be useful in making mobile robots work in real-world situations. Since the behaviors are responsible for managing the interactions between the robots and its environment, observing their use can be exploited to model these interactions. In our approach, the robot is initially given a set of "behavior-producing" modules to choose from, and the algorithm provides a memory-based approach to dynamically adapt the selection of these behaviors according to the history of their use. The approach is validated using a vision- and sonar-based Pioneer I robot in non-stationary conditions, in the context of a multi-robot foraging task. Results show the effectiveness of the approach in taking advantage of any regularities experienced in the world, leading to fast and adaptable specialization for the learning robot.