Learning from history for adaptive mobile robot control

Abstract

Learning in the mobile robot domain is a very challenging task, especially in nonstationary conditions. This paper presents an approach that allows a robot to learn a model of its interactions with its operating environment in order to manage them according to the experienced dynamics. The robot is initially given a set of "behavior-producing" modules to choose from, and the algorithm provides a means of making that choice intelligently and dynamically. The approach is validated using a vision- and sonar-based Pioneer I robot in non-stationary conditions, in the context of a multirobot 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.