Behavioral repertoire learning in robotics

Abstract

Behavioral Repertoire Learning in Robotics Antoine Cully ISIR, Université Pierre et Marie Curie-Paris 6, CNRS UMR 7222 4 place Jussieu, F-75252, Paris Cedex 05, France cully@isir.upmc.fr Jean-Baptiste Mouret ISIR, Université Pierre et Marie Curie-Paris 6, CNRS UMR 7222 4 place Jussieu, F-75252, Paris Cedex 05, France mouret@isir.upmc.fr ABSTRACT Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each con- troller with regard to this task (e.g. walking speed). How- ever, learning advanced, input-driven controllers (e.g. walk- ing in each direction) requires testing each controller on a large sample of the possible input signals. This costly pro- cess makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learn- ing technique that generates a behavioral repertoire by tak- ing advantage of the candidate solutions that are usually discarded. Instead of evolving a single, general controller, BR-evolution thus evolves a collection of simple controllers, one for each variant of the target behavior; to distinguish similar controllers, it uses a performance objective that al- lows it to produce a collection of diverse but high-performing behaviors. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.