SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909954413166592 |
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| author | Kushina, Nadezhda Watanabe, Ko Kannan, Aarthi Ashok, Ashita Dengel, Andreas Berns, Karsten |
| author_facet | Kushina, Nadezhda Watanabe, Ko Kannan, Aarthi Ashok, Ashita Dengel, Andreas Berns, Karsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking Kushina, Nadezhda Watanabe, Ko Kannan, Aarthi Ashok, Ashita Dengel, Andreas Berns, Karsten Robotics Artificial Intelligence Human-Computer Interaction 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. |
| title | SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking |
| topic | Robotics Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.08518 |