Graph-Based Reinforcement Learning for Robot Decision Making in Collaborative Robotics

Abstract

Human-Robot Collaboration (HRC) is increasingly becoming a core element in modern industrial automation, as it enables the flexibility needed to meet diverse and rapidly changing production demands. However, a key challenge lies in combining production efficiency and flexibility. Robot decision-making in HRC should not only minimize production time and costs, but also adapt to heterogeneous assembly scenarios and unpredictable human actions. This letter addresses these challenges with a Graph Convolutional Network (GCN)-based decision-making framework trained through a Reinforcement Learning (RL) procedure on a randomized set of assembly processes. The proposed RL-GCN optimizes long-term assembly efficiency by dynamically assigning robot tasks in real time to adapt to human choices. Also, an online fine-tuning stage customizes the model weights to the specific assembly, further enhancing performance and supporting real deployment. Extensive offline simulations and real-world experiments, including scenarios with dynamically changing assembly structures, demonstrate that the proposed method improves assembly efficiency while maintaining the flexibility required for robust HRC.