Active teaching for an interactive learning robot
Daisuke Katagami, Seiji Yamada · 2004
We have proposed a fast learning method that enables a mobile robot to acquire autonomous behaviors from interaction between human and robot. In this research we develop a behavior learning method ICS (interactive classifier system) using interactive evolutionary computation considering an operator's teaching cost. As a result, a mobile robot is able to quickly learn rules by directly teaching from an operator. ICS is a novel evolutionary robotics approach using classifier system. In this paper, we investigate teacher's physical and mental load and proposed a teaching method based on timing of instruction using ICS.