Continual Human-in-the-Loop Optimization

要旨

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization process. A more efficient approach would continually leverage data from previous users to accelerate optimization, exploiting shared traits while adapting to individual characteristics. We introduce the concept of Continual Human-in-the-Loop Optimization and a Bayesian optimization-based method that leverages a Bayesian-neural-network surrogate model to capture population-level characteristics while adapting to new users. We propose a generative replay strategy to mitigate catastrophic forgetting. We demonstrate our method by optimizing virtual reality keyboard parameters for text entry using direct touch, showing reduced adaptation times with a growing user base. Our method opens the door for next-generation personalized input systems that improve with accumulated experience.

受賞
Honorable Mention
著者
Yi-Chi Liao
ETH Zürich, Zürich, Switzerland
Paul Streli
ETH Zürich, Zürich, Switzerland
Zhipeng Li
ETH Zürich, Zurich, Switzerland
Christoph Gebhardt
ETH Zürich, Zürich, Switzerland
Christian Holz
ETH Zürich, Zurich, Switzerland
DOI

10.1145/3706598.3713603

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713603

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Optimization with/for AI

G318+G319
7 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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