Regularized GLISp for sensor-guided human-in-the-loop optimization
This work addresses sensor-guided optimization for human-in-the-loop systems, offering an incremental improvement by combining subjective feedback with quantitative data.
The paper tackled the problem of human-in-the-loop calibration by integrating sensor measurements into preference-based optimization, resulting in faster convergence and superior final solutions compared to baseline methods in benchmarks and a vehicle suspension tuning task.
Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While effective, methods such as Preferential Bayesian Optimization or Global optimization based on active preference learning with radial basis functions (GLISp) treat the system as a black box and ignore informative sensor measurements. In this work, we introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop through a physics-informed hypothesis function and a least-squares regularization term. This injects grey-box structure, combining subjective feedback with quantitative sensor information while preserving the flexibility of preference-based search. Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions compared to baseline GLISp.