Human Behavior Modeling During Dialogue by Using Generative Adversarial Networks

Abstract

In this research, human behavior during dialogue is modeled, with the goal of generating human-like motion for humanoid robots. Most of the previous studies on the human motion modeling aimed to model the single motion of human [8] and produce complicated behaviors by combining the models of the single motions [11]. In these studies, each model was associated with a label such as waving a hand, bowing and so on. However, since the human-like behavior in a daily life is diverse and ambiguous, defining a clear label for each motion of such behavior is difficult. To treat with this problem, in this paper, we collected the human motion data during a dialogue and propose a modeling method using Generative Adversarial Networks (GAN) [15]. The result of the human-robot experiment based on the subjective evaluation of the participants suggested that the human-like behavior of the robot could be generated by the proposed method.