ヒューリスティックを超えて：ヒューマン・イン・ザ・ループ・シミュレーションにおける物理的ヒューマンロボットインタラクションのための標準化Real2Simパイプライン
Beyond Heuristics: A Standardized Real2Sim Pipeline for Physical Human Robot Interaction in Human-in-the-Loop Simulation
歩行支援ロボットの骨盤-ストラップインターフェースの物理的相互作用モデルを、被験者ごとのデータからCMA-ESを用いて同定し、未見の被験者にも適用可能な標準化パイプラインを提案した。
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著者: Chengyuan Yang, Yifan Wang, Chun Kwang Tan, Sherwin Stephen Chan, Youlong Wang, Xiaoyue Yan, Lei Li, Wei Tech Ang
分類: cs.RO
原文アブストラクト
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.
