Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach

摘要

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. • Designed a fast-converging, task-agnostic Q-learning algorithm for REBA. • Optimized object handover coordinates for ergonomics safety in bimanual pHRI. • Developed a high-fidelity inverse kinematics method for human posture simulation. • Developed a VR framework using inverse kinematics tailored to the worker’s physique. • Introduced alternative ways and circumventions towards ergonomic optimization.