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

TL;DR

This paper introduces DULA and DEBA, differentiable ergonomic risk models that accurately replicate RULA and REBA assessments, enabling efficient postural optimization in human-robot interaction.

Contribution

The paper presents novel differentiable ergonomic assessment models, DULA and DEBA, which improve computational efficiency while maintaining high accuracy in postural evaluation.

Findings

DULA and DEBA achieve over 99% accuracy in replicating RULA and REBA.

The models enable effective postural optimization in simulated tasks.

Framework validated through human and simulation experiments.

Abstract

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.