Mutual entrainment: Implicit elicitation of human gestures by robot speech

Social robots that provide services to humans in real environments have been developed in recent years. Such a robot should appropriately recognize its users' orders through human-like communications because of user-friendliness. However, their styles of communicating are too diverse to achieve this goal. If the robot could shape their styles, its recognition ability would be improved. An entrainment, which is a phenomenon where human's behavior is synchronized with robot's behavior, can be useful for this shaping. Previous studies have reported the entrainment occurring in the same modality, but they have given little attention to entrainment across different modalities (e.g., speech and gestures). We need to consider this cross-modal effect because human-robot interaction is inherently multi-modal. In this paper, we defined “mutual entrainment” as the entrainment across different modalities and investigated the effect of it through a laboratory experiment. We evaluate how the frequency of human pointing gestures varies with the amount of information in robot speech, and as a result, we found that the gesture frequency increased as the amount of information decreased. The results suggest that smoother human-robot communications can be achieved by shaping human behavior through mutual entrainment.