arXiv cs.AI· Harun Tolasa, Gorkem Gemalmaz, Volkan Patoglu·· 2 天前AI 评分15
基于主动学习的分阶粘弹性模型参数优化实现真实触觉渲染
Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering
AI 导读
研究提出用主动学习结合人在回路（HiL）优化，确定分阶粘弹性模型参数以提升触觉渲染的真实感。该方法通过定性反馈为每个个体优化参数，再将HiL结果聚合为感知图，从中选出在一般人群中广泛被认为真实的群体级最优参数。针对三种粘弹性材料的实验验证了该方法的有效性，有望改善医学训练模拟器的sim-to-real迁移性能。
正文 · 原文
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.
| Comments: | This work has been submitted to the IEEE Transactions on Haptics for possible publication. 14 pages, 8 figures |
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2512.00667 [eess.SY] |
| (or arXiv:2512.00667v2 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2512.00667 arXiv-issued DOI via DataCite |
Submission history
From: Volkan Patoglu [view email]
[v1]
Sat, 29 Nov 2025 23:38:19 UTC (6,760 KB)
[v2]
Tue, 19 May 2026 16:13:18 UTC (7,502 KB)
来源：arXiv cs.AI · arxiv.org