Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering

TL;DR

This paper introduces a human-in-the-loop active learning approach to optimize fractional-order viscoelastic model parameters, enhancing the realism of haptic rendering in medical simulators for biological tissues.

Contribution

It presents a systematic method combining active learning and perceptual mapping to determine population-level fractional-order model parameters for realistic haptic feedback.

Findings

Active learning effectively optimizes model parameters for individual realism.

Perceptual maps enable selection of broadly realistic parameters.

Experimental results validate improved perceived realism across materials.

Abstract

Effective medical simulators necessitate realistic haptic rendering of biological tissues that exhibit viscoelastic material properties, such as creep and stress relaxation. Fractional-order models provide an effective means of describing intrinsically time-dependent viscoelastic dynamics with few parameters, as they naturally capture memory effects. However, due to the unintuitive, frequency-dependent coupling among the order of the fractional element and other parameters, determining appropriate parameter values for fractional-order models that yield high perceived realism remains a significant challenge. In this study, we propose a systematic means of determining the parameters of fractional-order viscoelastic models that optimizes the perceived realism of haptic rendering across general populations. First, we demonstrate that the parameters of fractional-order models can be effectively optimized through active learning, using qualitative feedback-based human-in-the-loop (HiL) optimization, to ensure consistently high realism ratings for each individual. Second, we propose a rigorous method to combine HiL optimization results into an aggregate perceptual map trained on the entire dataset, and demonstrate how to select population-level optimal parameters from this representation that are broadly perceived as realistic across general populations. Finally, we provide evidence of the effectiveness of the generalized fractional-order viscoelastic model parameters for three viscoelastic materials by characterizing their perceived realism through human-subject experiments. Overall, generalized fractional-order viscoelastic models established through the proposed HiL optimization and aggregation approach possess the potential to significantly improve the sim-to-real transition performance of medical training simulators.