In this paper, we describe influence of viewpoints of observation in an interactive evolutionary robotics system. We have been proposed a behavior learning system ICS (Interactive Classifier System) using interactive evolutionary computation. In this system, a mobile robot is able to quickly learn rules by direct teaching of a human operator. ICS is a novel evolutionary robotics approach using a classifier system. We classify teaching methods into internal observation and external one, and investigate influence of observation methods. We have experiments based on our teaching methods in two kinds of tasks. We found that teaching methods from different viewpoints of observation change teaching efficiency because of the difference between a robot's recognition and an operator's one in an environment.
['Daisuke Katagami', 'Seiji Yamada']
Interactive evolutionary robotics from different viewpoints of observation