The Rheology Gap: A Component-Level Benchmark for VR-to-Reality Motor Skill Transfer under Linear Haptic Simulation.

IF 6.5
Zhiping Liu, Lu An
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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.

流变差距:线性触觉模拟下vr到现实运动技能转移的组件级基准。
无源性限制限制了力反馈设备可以稳定显示的阻抗范围,因此实时触觉训练默认为线性物理-通常是质量-弹簧-阻尼器(MSD)模型-尽管值得训练的材料是非线性和非牛顿的。我们将这种差异定义为流变差距,并询问哪些运动技能组件在手工面团拉伸任务的故意线性渲染中幸存下来。实例内设计在五种情况下(N=378美元)改变VR因素时保持任务不变;真实世界的转移是在一个单独的队列中进行评估的(n=90),由盲法认证的评委(avpn衍生的评分标准)在正宗面团上进行评估,并以公布的流变学作为物质力参考。相对于视频教学,VR训练产生了中等的现实世界效益(Cohen’s $d=0.76$):程序和空间组件转移,而材料特定的力调制不具有厚度均匀性($-$41.6%)和工艺效率($-$45.6%),最流变敏感的组件显示出最大的缺陷。非信息性的力反馈损害了学习,并且在24小时内持续获得增益。由于条件随几个因素的变化而变化,因此报告的效果是复合的。我们的核心贡献是一个可重复的、组件级基准任务协议、客观指标、特征线性基线和已发布的流变学参考,在此基础上,任何未来的非线性引擎(FEM、MPM、XPBD、神经物理)都可以在相同的条件下评分,使模拟保真度可证伪。作为第一个应用,在时域无源控制下,模拟阶段的原型在1 kHz稳定运行,并将力保真度误差从41.0%降至24.6% NRMSE。在对应变强化假设(离线$R^{2}=0.31$)进行闭环验证之前,目前的VR最好定位为学徒前的脚手架,而不是物理实践的替代品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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