A Practical, Robust, Accurate Gaze-Based Intention Inference Method for Everyday Human-Robot Interaction

Haoyang Xu, Tao Wang, Yingjie Chen, Tianze Shi

Abstract: Gaze estimation is a crucial component of human-robot Interaction (HRI). While previous gaze estimation methods have been widely applied in advertising and gaming with head-mounted devices or complicated camera systems, little research has been conducted in everyday HRI scenarios. During interactions, robots have the potential to infer human intention through static gaze directions and dynamic eye movements, enabling them to behave more intelligently and friendly. This paper combines appearance-based gaze estimation methods with eye movement analysis methods to infer human intentions, particularly in human-robot interaction scenarios. Real interactions were conducted to test the accuracy and robustness of the methods developed. The experiments demonstrate that our methods deliver practical, robust, and accurate results.