DULAとDEBA：人間工学的に知的なpHRIにおける姿勢評価と最適化のための微分可能な人間工学リスクモデル
DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI
検証済みのRULA/REBA評価を99%以上の精度で再現する連続微分可能な人間工学モデルDULAとDEBAを学習し、姿勢最適化に計算上の利点をもたらす枠組みを提案した論文。
著者: Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather, Tucker Hermans
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in performing postural optimization. In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction. We introduce DULA and DEBA, differentiable and continuous ergonomics models learned to replicate the popular and scientifically validated RULA and REBA assessments with more than 99% accuracy. We show that DULA and DEBA provide assessment comparable to RULA and REBA while providing computational benefits when being used in postural optimization. We evaluate our framework through human and simulation experiments. We highlight DULA and DEBA's strength in a demonstration of postural optimization for a simulated pHRI task.
