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This content will become publicly available on December 1, 2025

Title: Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach
Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented.  more » « less
Award ID(s):
2047138
PAR ID:
10617085
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier - Automation in Construction
Date Published:
Journal Name:
Automation in Construction
Volume:
168
Issue:
PA
ISSN:
0926-5805
Page Range / eLocation ID:
105741
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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