Search papers, labs, and topics across Lattice.
This paper introduces Vibrotactile Preference Learning (VPL), a Gaussian process-based preference learning system that personalizes vibrotactile feedback by modeling user preferences and uncertainties. VPL employs an expected information gain-based acquisition function to select pairwise comparisons, optimizing for efficient exploration of vibrotactile parameter spaces. A user study (N=13) using Xbox controller feedback demonstrates VPL's ability to learn individualized preferences with low user burden, suggesting a path toward scalable personalization of haptic experiences.
Stop guessing what feels good: this system learns personalized vibration preferences from just 40 pairwise comparisons.
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.
