Collaborative Robot Control Based on Human Gaze Tracking - Francesco Di Stefano; Alice Giambertone; Laura Salamina; Matteo Melchiorre; Stefano Mauro (Sensors)
Gaze tracking is gaining relevance in collaborative robotics as a means to enhance human–machine interaction by enabling intuitive and non-verbal communication. This study explores the integration of human gaze into collaborative robotics by demonstrating the possibility of controlling a robotic manipulator with a practical and non-intrusive setup made up of a vision system and gaze-tracking software. After presenting a comparison between the major available systems on the market, OpenFace 2.0 was selected as the primary gaze-tracking software and integrated with a UR5 collaborative robot through a MATLAB-based control framework. Validation was conducted through real-world experiments, analyzing the effects of raw and filtered gaze data on system accuracy and responsiveness. The results indicate that gaze tracking can effectively guide robot motion, though signal processing significantly impacts responsiveness and control precision. This work establishes a foundation for future research on gaze-assisted robotic control, highlighting its potential benefits and challenges in enhancing human–robot collaboration.