Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918462779031552 |
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| author | Zhang, Rongtao Zhu, Xin Khotbehsara, Masoume Pourebadi Dao, Warren Bıyık, Erdem Culbertson, Heather |
| author_facet | Zhang, Rongtao Zhu, Xin Khotbehsara, Masoume Pourebadi Dao, Warren Bıyık, Erdem Culbertson, Heather |
| contents | Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference learning. VPL uses an expected information gain-based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user-reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in a user study (N = 13) using the vibrotactile feedback from a Microsoft Xbox controller, showing that it efficiently learns individualized preferences while maintaining comfortable, low-workload user interactions. These results highlight the potential of VPL for scalable personalization of vibrotactile experiences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20210 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback Zhang, Rongtao Zhu, Xin Khotbehsara, Masoume Pourebadi Dao, Warren Bıyık, Erdem Culbertson, Heather Human-Computer Interaction Artificial Intelligence Machine Learning Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference learning. VPL uses an expected information gain-based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user-reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in a user study (N = 13) using the vibrotactile feedback from a Microsoft Xbox controller, showing that it efficiently learns individualized preferences while maintaining comfortable, low-workload user interactions. These results highlight the potential of VPL for scalable personalization of vibrotactile experiences. |
| title | Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.20210 |