Lifelong robot learning

Sebastian B. Thrun 2 University of Bonn Institut für Informatik III Römerstr. 164, 53117 Bonn, Germany E-mail: [email protected] Tom M. Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, U.S.A. E-mail: [email protected] Abstract— Learning provides a useful tool for the automatic design of autonomous robots. Recent research on learning robot control has predominantly focussed on learning single tasks that were studied in isolation. If robots encounter a multitude of control learning tasks over their entire lifetime, however, there is an opportunity to transfer knowledge between them. In order to do so, robots may learn the invariants of the individual tasks and environments. This task-independent knowledge can be employed to bias generalization when learning control, which reduces the need for real-world experimentation. We argue that knowledge transfer is essential if robots are to learn control with moderate learning times in complex scenarios. Two approaches to lifelong robot learning which both capture invariant knowledge about the robot and its environments are reviewed. Both approaches have been evaluated using a HERO-2000 mobile robot. Learning tasks included navigation in unknown indoor environments and a simple find-andfetch task.