@article{ref1,
title="Mitigating the risk of musculoskeletal disorders during human robot collaboration: a reinforcement learning approach",
journal="Proceedings of the Human Factors and Ergonomic Society annual meeting",
year="2022",
author="Xie, Ziyang and Lu, Lu and Wang, Hanwen and Su, Bingyi and Liu, Yunan and Xu, Xu",
volume="66",
number="1",
pages="1543-1547",
abstract="Work-related musculoskeletal disorders (MSDs) are often observed in human-robot collaboration (HRC), a common work configuration in modern factories. In this study, we aim to reduce the risk of MSDs in HRC scenarios by developing a novel model-free reinforcement learning (RL) method to improve workers? postures. Our approach follows two steps: first, we adopt a 3D human skeleton reconstruction method to calculate workers? Rapid Upper Limb Assessment (RULA) scores; next, we devise an online gradient-based RL algorithm to dynamically improve the RULA score. Compared with previous model-based studies, the key appeals of the proposed RL algorithm are two-fold: (i) the model-free structure allows it to ?learn? the optimal worker postures without need any specific biomechanical models of tasks or workers, and (ii) the data-driven nature makes it accustomed to arbitrary users by providing personalized work configurations. <br><br>RESULTS of our experiments confirm that the proposed method can significantly improve the workers? postures.<p /> <p>Language: en</p>",
language="en",
issn="2169-5067",
doi="10.1177/1071181322661151",
url="http://dx.doi.org/10.1177/1071181322661151"
}