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.

翻译：振动触觉感知的个体差异凸显了随着触觉反馈在交互系统中日益普及，个性化的重要性不断提升。我们提出振动触觉偏好学习（VPL）系统，该系统通过基于高斯过程的不确定性感知偏好学习，捕捉用户对振动触觉参数的个性化偏好空间。VPL采用基于期望信息增益的采集策略，引导用户在40轮整体偏好两两比较中进行查询选择，并辅以用户报告的不确定性，从而实现对参数空间的高效探索。我们通过一项用户研究（N=13），使用微软Xbox控制器的振动触觉反馈对VPL进行评估，结果表明该系统能够高效学习个性化偏好，同时保持舒适、低工作量的用户交互。这些结果突显了VPL在实现振动触觉体验的可扩展个性化方面的潜力。