Incremental Learning of Depictive Gestures in Collaborative Human-Robot Interaction

Abstract

Human-like conversation with gestures and verbal cues makes a contribution to provide more natural communication. In this study, we proposed an approach for robot's imitative learning in human-robot interaction. This paper presents a method of robot motion generation based on a steady-state genetic algorithm (SSGA). SSGA is one of evolutionary optimization methods using selection, mutation, and crossover operators. Moreover, we discuss the applicability of the proposed approach to incremental learning of depictive gestures in collaborative human-robot interaction. We show some experimental examples to discuss it in this paper.