Real Robot Learning with Human Teaching
Daisuke Katagami, Seiji Yamada · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2002
In this paper, 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 methods which adds new acquired rules to priori knowledge by teaching from human-robot interaction at any time.