Evolving effective visual tracking through shaping

Abstract

Shaping is a way in which a human designer can provide assistance to a learning system to enable it to solve problems that would otherwise defeat it. Results are presented showing that shaping can significantly improve the final performance of controllers evolved for a difficult visual tracking task. Controllers are developed in simulation and then transferred to a real robot head.