Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
This addresses the need for scalable personalization of haptic feedback in interactive systems for users with individual perceptual differences, representing an incremental improvement in preference learning methods.
The paper tackles the problem of personalizing vibrotactile feedback by proposing Vibrotactile Preference Learning (VPL), a system that learns user-specific preferences over vibrotactile parameters through Gaussian-process-based uncertainty-aware learning and pairwise comparisons over 40 rounds, evaluated in a user study with 13 participants showing efficient learning of individualized preferences.
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.