Interactive classifier system for real robot learning

Abstract

We describe a fast learning method for a mobile robot which acquires autonomous behaviors from interaction between a human and a robot. We develop a behavior learning method ICS (interactive classifier system) using evolutionary computation and a mobile robot is able to quickly learn rules so that a human operator can directly teach a physical robot. Also the ICS is a novel evolutionary robotics approach, using an adaptive classifier system, to environmental changes. The ICS has two major characteristics for evolutionary robotics. For one thing, it can speedup learning by means of generating initial individuals from human-robot interaction. For another, it is a kind of incremental learning method which adds new acquired rules to priori knowledge by teaching from human-robot interaction at any time.