Robotic social imitation depends on self-embodiment and self-evaluation by direct teaching under multiple instructors

Abstract

We have been worked about robotic social imitation in order to learn self-behavior depending on self-embodiment through interaction with multiple human. In this paper, we propose a learning method which allows to select behavioral patterns depending on self-embodiment and self-evaluation from multiple instructors by using the robot simulator Webots. We confirmed that results demonstrated that our proposal allows to improve the representative teaching data by the clustering includes two evaluation values (the distance moved forward and the impact shock for the body).