基于用户反馈的自动标定，提升人机交互仿真的真实度。
Beyond Heuristics: A Standardized Real2Sim Pipeline for Physical Human Robot Interaction in Human-in-the-Loop Simulation
- 用用户舒适反馈作为目标，通过进化算法自动识别12个力学参数。
- 仅需微调5个参数即可适配新用户，且能准确复现真实运动反应。
- 适合个性化康复机器人控制器预验证，解决传统经验调参问题。
老龄化推动辅助机器人需求，但物理测试存在安全风险与成本高问题，使得人机协同仿真成为替代方案。然而，现有方法依赖经验式调参的交互模型限制了系统耦合仿真精度。本文提出一种从真实到仿真（Real2Sim）标准化流程，针对移动平衡辅助设备的骨盆-束带接口，建模为6自由度粘弹性机构，并利用协方差矩阵自适应进化策略（CMA-ES）对每位受试者12个方向的刚度与阻尼参数进行数据驱动识别。以用户“安全且舒适”反馈作为可重复的操作点，解决了不同体型下的束带松紧歧义问题。在五名受试者队列上进行组内相关性分析，分离出可共享与个体特异性参数，从而获得基于已有数据的先验参数集。使用该先验参数配置未见受试者时，仅需优化其中5个参数。校准后的模型能准确再现真实交互范围，并诱导出生物力学合理的步态适应变化。而过度柔软或过紧的设置则表现不佳，验证了正确操作点的存在——这是任何经验调参都无法可靠选择的。该流程显著提升了人机协同仿真保真度，支持数字人孪生作为个性化控制器临床前验证的预测工具。
原文摘要 · Abstract (English)
The aging global population drives demand for assistive robots, yet the safety risks and costs of physical testing make Human-in-the-Loop (HITL) simulation an attractive alternative. Its fidelity for coupled systems, however, is limited by interaction models whose impedance parameters are tuned heuristically rather than identified from data. We present a Real2Sim pipeline that identifies the coupled Physical Human-Robot Interaction (pHRI) dynamics of a pelvis--strap interface on an overground mobile balance assistant. The interface is modeled as a 6-DoF viscoelastic mechanism whose 12 directional stiffness and damping parameters are identified per subject via Covariance Matrix Adaptation Evolution Strategy (CMA-ES), using the user's ``Safe \& Comfortable'' feedback as a reproducible operating point that resolves harness-tightness ambiguity across anthropometrics. An intraclass-correlation analysis over a five-subject cohort separates shareable from subject-specific parameters, yielding a set of prior parameters derived from the existing data. Deploying this prior configures a previously unseen subject by refining only 5 of the 12 parameters. The calibrated model then reproduces the real interaction envelope and induces biomechanically accurate gait adaptations in the Human Digital Twin (HDT). Overly compliant and overly stiff settings, by contrast, fail as extreme settings, confirming a correct operating point that no heuristic tuning procedure can reliably select. The pipeline thus improves HITL simulation fidelity and supports the Human Digital Twin as a predictive tool for pre-clinical verification of personalized controllers.
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