Ergonomic Optimization in Worker-Robot Bimanual Object Handover: Implementing REBA Using Reinforcement Learning in Virtual Reality

TL;DR

This paper introduces a reinforcement learning framework trained in virtual reality to optimize ergonomic safety during worker-robot object handovers, improving upon traditional assessment methods like REBA.

Contribution

It presents a novel RL-based approach for ergonomic optimization in pHRI, with a rigorous mathematical structure and generalizability to various tasks and humans.

Findings

The framework accurately predicts ergonomic scores in real-time.

It outperforms naive heuristics in object handover tasks.

The virtual reality training enables effective generalization.

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.