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.
振动触觉偏好学习：面向个性化振动反馈的不确定性感知偏好学习 / Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
本文提出了一种名为振动触觉偏好学习（VPL）的系统，它利用基于高斯过程的不确定性感知偏好学习，通过40轮两两比较和用户报告的不确定性，高效地学习每个人对振动反馈的独特偏好，并在Xbox控制器上实验验证了其低工作量和舒适性，为大规模个性化触觉体验提供了可行方案。
源自 arXiv: 2604.20210
