Analysis of Role-Based Gaze Behaviors and Gaze Aversions, and Implementation of Robot’s Gaze Control for Multi-party Dialogue

In a multi-person face-to-face dialogue, people naturally gaze towards others or avert their gazes, according to their dialogue roles and mental states. The goal of this research is to develop a robot/agent that can generate human-like eye movements, in order to achieve smoother and more engaged dialogue interactions with multiple users. In this study, we analyze the gaze behaviors in three-party dialogue data, accounting for turn-taking, dialogue roles and gaze aversions during the dialogue interactions. Based on the analysis results, we implemented gaze models on a humanoid robot. Subjective evaluation experiments showed that natural behaviors are achieved by our proposed gaze control system, which accounts for dialogue roles and eyeball movement control.