Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners

The research and development of robot partners have been actively conducted to support human daily life. Human-robot interaction is one of the important research field, in which verbal and nonverbal communication are essential elements for improving the interactions between humans and robots. Thus, the purpose of this research was to establish a method to adapt a human-robot interaction mechanism for robot partners to various situations. In the proposed system, the robot needs to analyze the gestures of humans to interact with them. Humans have the ability to interact according to dynamically changing environmental conditions. Therefore, when robots interact with a human, it is necessary for robots to interact appropriately by correctly judging the situation according to human gestures to carry out natural human-robot interaction. In this paper, we propose a constructive methodology on a system that enables nonverbal communication elements for human-robot interaction. The proposed method was validated through a series of experiments.