{"ID":2831169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08518","arxiv_id":"2512.08518","title":"SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking","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.","short_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 com...","url_abs":"https://arxiv.org/abs/2512.08518","url_pdf":"https://arxiv.org/pdf/2512.08518v2","authors":"[\"Nadezhda Kushina\",\"Ko Watanabe\",\"Aarthi Kannan\",\"Ashita Ashok\",\"Andreas Dengel\",\"Karsten Berns\"]","published":"2025-12-09T12:08:21Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.HC\"]","methods":"[\"Transformer\"]","has_code":false}