SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking

• URL: http://arxiv.org/abs/2512.08518v2
• Date: Wed, 10 Dec 2025 11:46:50 GMT
• Title: SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking
• Authors: Nadezhda Kushina, Ko Watanabe, Aarthi Kannan, Ashita Ashok, Andreas Dengel, Karsten Berns,
• Abstract summary: We investigate user comfort with the robot "Ameca" across four experimentally controlled distances.<n>We evaluate multiple machine learning and deep learning models to estimate comfort based on gaze features.
• Score: 4.527641435286577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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