SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking
This work addresses user comfort for social robots, but it is incremental as it applies known eye-tracking methods to a new robot context.
The study tackled the problem of estimating user comfort in human-robot interactions by using eye-tracking features, finding that a Decision Tree classifier achieved the highest performance with an F1-score of 0.73, indicating that physiological comfort thresholds differ from human-human dynamics.
Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in human-human interactions, their applicability to interactions with humanoid robots remains unexplored. In this study, we investigate user comfort with the robot "Ameca" across four experimentally controlled distances (0.5 m to 2.0 m) using mobile eye-tracking and subjective reporting (N=19). We evaluate multiple machine learning and deep learning models to estimate comfort based on gaze features. Contrary to previous human-human studies where Transformer models excelled, a Decision Tree classifier achieved the highest performance (F1-score = 0.73), with minimum pupil diameter identified as the most critical predictor. These findings suggest that physiological comfort thresholds in human-robot interaction differ from human-human dynamics and can be effectively modeled using interpretable logic.