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Ergonomics and human comfort are essential concerns in physical human-robotinteraction applications. Defining an accurate and easy-to-use ergonomicassessment model stands as an important step in providing feedback for posturalcorrection to improve operator health and comfort. Common practical methods inthe area suffer from inaccurate ergonomics models in performing posturaloptimization. In order to retain assessment quality, while improvingcomputational considerations, we propose a novel framework for posturalassessment and optimization for ergonomically intelligent physical human-robotinteraction. We introduce DULA and DEBA, differentiable and continuousergonomics models learned to replicate the popular and scientifically validatedRULA and REBA assessments with more than 99% accuracy. We show that DULA andDEBA provide assessment comparable to RULA and REBA while providingcomputational benefits when being used in postural optimization. We evaluateour framework through human and simulation experiments. We highlight DULA andDEBA's strength in a demonstration of postural optimization for a simulatedpHRI task.

DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI