通过不确定性感知学习，个性化定制振动反馈偏好。
Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
- 基于高斯过程的不确定性感知方法，建模用户偏好空间。
- 40轮配对比较下高效学习个体偏好，用户负荷低。
- 适合需要个性化触觉反馈的交互系统开发人员。
个体在振动触觉感知上的差异凸显了个性化的重要性，随着触觉反馈在交互系统中日益普及。我们提出振动偏好学习（Vibrotactile Preference Learning, VPL），一种基于高斯过程的不确定性感知偏好学习方法，通过40轮配对比较和用户报告的不确定性，以期望信息增益策略指导查询选择，高效探索振动参数空间。在13名用户的实验中，使用微软Xbox控制器的触觉反馈验证了VPL能有效学习个性化偏好，同时保持舒适、低工作量的交互体验。结果表明，VPL具有实现振动体验规模化个性化的潜力。
原文摘要 · Abstract (English)
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.
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