Ergonomic Optimization in Worker-Robot Bimanual Object Handover: Implementing REBA Using Reinforcement Learning in Virtual Reality
Mani Amani, Reza Akhavian
TL;DR
The paper addresses ergonomic safety in pHRI handover tasks by overcoming REBA's non-differentiability through a reinforcement-learning framework trained in a VR environment with IK-driven humanoids. It combines Q-Learning on a finite-MDP with a Unity-based digital twin to locate optimal handover coordinates that minimize postural risk, using REBA-derived postural scores as the reward signal. In-lab VR experiments show the optimized handovers reduce postural risk compared with ergonomic baselines, and the authors discuss generalizability, limitations, and avenues for future advancement toward deeper RL methods and real-world deployment. The work provides a practical path to integrate ergonomic safety into autonomous handover in cluttered construction environments, enabling worker-specific adaptation and safer pHRI in industry contexts.
Abstract
Robots can serve as safety catalysts on construction job sites by taking over hazardous and repetitive tasks while alleviating the risks associated with existing manual workflows. Research on the safety of physical human-robot interaction (pHRI) is traditionally focused on addressing the risks associated with potential collisions. However, it is equally important to ensure that the workflows involving a collaborative robot are inherently safe, even though they may not result in an accident. For example, pHRI may require the human counterpart to use non-ergonomic body postures to conform to the robot hardware and physical configurations. Frequent and long-term exposure to such situations may result in chronic health issues. Safety and ergonomics assessment measures can be understood by robots if they are presented in algorithmic fashions so optimization for body postures is attainable. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for many decades, they lack a rigorous mathematical structure which poses challenges in using them immediately for pHRI safety optimization purposes. Furthermore, learnable approaches have limited robustness outside of their training data, reducing generalizability. In this paper, we propose a novel framework that approaches optimization through Reinforcement Learning, ensuring precise, online ergonomic scores as compared to approximations, while being able to generalize and tune the regiment to any human and any task. To ensure practicality, the training is done in virtual reality utilizing Inverse Kinematics to simulate human movement mechanics. Experimental findings are compared to ergonomically naive object handover heuristics and indicate promising results where the developed framework can find the optimal object handover coordinates in pHRI contexts for manual material handling exemplary situations.