{"title":"The Rheology Gap: A Component-Level Benchmark for VR-to-Reality Motor Skill Transfer under Linear Haptic Simulation.","authors":"Zhiping Liu, Lu An","doi":"10.1109/TVCG.2026.3745621","DOIUrl":null,"url":null,"abstract":"<p><p>Passivity constraints bound the impedance range that force-feedback devices can stably display, so real-time haptic training defaults to linear physics-typically a mass-spring-damper (MSD) model-although materials worth training on are nonlinear and non-Newtonian. We define this discrepancy as the rheology gap and ask which motor-skill components survive a deliberately linearized rendering of an artisanal dough-stretching task. A within-instance design held the task fixed while varying VR factors across five conditions ($N=378$); real-world transfer was assessed in a separate cohort ($n=90$) evaluated on authentic dough by blinded certified judges (AVPN-derived rubric), with published rheometry as the material force reference. Relative to video instruction, VR training produced a medium real-world benefit (Cohen's $d=0.76$): procedural and spatial components transferred, whereas material-specific force modulation did not-thickness uniformity ($-$41.6%) and process efficiency ($-$45.6%), the most rheology-sensitive components, showed the largest deficits. Non-informative force feedback impaired learning, and gains persisted at 24 h. Because conditions covary on several factors, effects are reported as composite. Our central contribution is a reproducible, component-level benchmark-task protocol, objective metrics, characterized linear baseline, and published-rheometry reference-on which any future nonlinear engine (FEM, MPM, XPBD, neural physics) can be scored under identical conditions, rendering simulation fidelity falsifiable. As a first application, a simulation-stage prototype rendering the calibrated strain-stiffening law under time-domain passivity control runs stably at 1 kHz and cuts force-fidelity error from 41.0% to 24.6% NRMSE. Pending closed-loop validation of the strain-stiffening hypothesis (offline $R^{2}=0.31$), current VR is best positioned as pre-apprenticeship scaffolding, not a substitute for physical practice.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on visualization and computer graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TVCG.2026.3745621","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Passivity constraints bound the impedance range that force-feedback devices can stably display, so real-time haptic training defaults to linear physics-typically a mass-spring-damper (MSD) model-although materials worth training on are nonlinear and non-Newtonian. We define this discrepancy as the rheology gap and ask which motor-skill components survive a deliberately linearized rendering of an artisanal dough-stretching task. A within-instance design held the task fixed while varying VR factors across five conditions ($N=378$); real-world transfer was assessed in a separate cohort ($n=90$) evaluated on authentic dough by blinded certified judges (AVPN-derived rubric), with published rheometry as the material force reference. Relative to video instruction, VR training produced a medium real-world benefit (Cohen's $d=0.76$): procedural and spatial components transferred, whereas material-specific force modulation did not-thickness uniformity ($-$41.6%) and process efficiency ($-$45.6%), the most rheology-sensitive components, showed the largest deficits. Non-informative force feedback impaired learning, and gains persisted at 24 h. Because conditions covary on several factors, effects are reported as composite. Our central contribution is a reproducible, component-level benchmark-task protocol, objective metrics, characterized linear baseline, and published-rheometry reference-on which any future nonlinear engine (FEM, MPM, XPBD, neural physics) can be scored under identical conditions, rendering simulation fidelity falsifiable. As a first application, a simulation-stage prototype rendering the calibrated strain-stiffening law under time-domain passivity control runs stably at 1 kHz and cuts force-fidelity error from 41.0% to 24.6% NRMSE. Pending closed-loop validation of the strain-stiffening hypothesis (offline $R^{2}=0.31$), current VR is best positioned as pre-apprenticeship scaffolding, not a substitute for physical practice.