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DTSTAMP:20221109T183714Z
LOCATION:A706
DTSTART;TZID=America/New_York:20221013T134800
DTEND;TZID=America/New_York:20221013T140600
UID:HFESAM_HFES 66th International Annual Meeting_sess176_LECT243@linkling
 s.com
SUMMARY:Mitigating the risk of musculoskeletal disorders during human robo
 t collaboration: a reinforcement learning approach
DESCRIPTION:Lecture\n\nMitigating the risk of musculoskeletal disorders du
 ring human robot collaboration: a reinforcement learning approach\n\nXie, 
 Lu, Wang, Su, Liu...\n\nWork-related musculoskeletal disorders (MSDs) are 
 often observed in human-robot collaboration (HRC), a common work configura
 tion 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 wor
 kers’ Rapid Upper Limb Assessment (RULA) scores; next, we devise an online
  gradient-based RL algorithm to dynamically improve the RULA score. Compar
 ed with previous model-based studies, the key appeals of the proposed RL a
 lgorithm are two-fold: (i) the model-free structure allows it to “learn” t
 he optimal worker postures without need any specific biomechanical models 
 of tasks or workers, and (ii) the data-driven nature makes it accustomed t
 o arbitrary users by providing personalized work configurations. Results o
 f our experiments confirm that the proposed method can significantly impro
 ve the workers’ postures.\n\nTrack: Human AI Robot Teaming (HART), Human P
 erformance Modeling
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