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Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering — arXiv:2512.00667

Source: arXiv (en)
Original: https://arxiv.org/abs/2512.00667
License (as archived): CC0-1.0
Archived: 2026-10-16T09:06:03.661Z
SHA-256 of the archived text: 94d1d7c051db39fffd0a9ea161d8108188e088068f89a6a11b2effe622de47e2
Stable URL of this page: https://metraipolis.com/documento/dc_1be2edfffa0b

How to cite

Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering — arXiv:2512.00667 — arXiv (en), https://arxiv.org/abs/2512.00667 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1be2edfffa0b (Metraipolis archive dc_1be2edfffa0b, sha256 94d1d7c051db39fffd0a9ea161d8108188e088068f89a6a11b2effe622de47e2)

BibTeX

@misc{dc1be2edfffa0b,
  title = {Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering — arXiv:2512.00667},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2512.00667},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1be2edfffa0b; sha256 94d1d7c051db39fffd0a9ea161d8108188e088068f89a6a11b2effe622de47e2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2512.00667. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Systems and Control (eess.SY) ; Artificial Intelligence (cs.AI); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2512.00667 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:17.847Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).