Beyond Heuristics: A Standardized Real2Sim Pipeline for Physical Human Robot Interaction in Human-in-the-Loop Simulation

2026-07-03

AI Summary

Existing human-in-the-loop simulation approaches rely heavily on heuristic parameter tuning and lack data-driven personalization, resulting in insufficient fidelity. This work proposes a Real2Sim standardization pipeline that leverages user feedback on “safety and comfort” to identify a 12-dimensional set of individualized parameters at the pelvis-harness interface, using a six-degree-of-freedom viscoelastic model optimized via the CMA-ES algorithm. Intra-class correlation analysis distinguishes universal from subject-specific parameters, while a reproducible operating point eliminates ambiguity in harness tension. Remarkably, only five parameters require calibration to adapt the model to a new user. The calibrated model accurately reproduces real-world interaction envelopes and elicits biomechanically plausible gait adaptations, significantly enhancing simulation fidelity and enabling preclinical validation of personalized controllers.

Abstract

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.

Problem

• Human-in-the-Loop simulation
• Physical Human-Robot Interaction
• impedance parameter identification
• simulation fidelity
• Human Digital Twin

Innovation

• Real2Sim
• Physical Human-Robot Interaction
• Human Digital Twin
• Covariance Matrix Adaptation Evolution Strategy
• Viscoelastic Modeling