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Title: Mitigating the risk of musculoskeletal disorders during human robot collaboration: a reinforcement learning approach
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. Results of our experiments confirm that the proposed method can significantly improve the workers’ postures.  more » « less
Award ID(s):
2024688
PAR ID:
10417875
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
66
Issue:
1
ISSN:
2169-5067
Page Range / eLocation ID:
1543 to 1547
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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