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This paper presents a Graph Convolutional Network (GCN)-based decision-making framework for Human-Robot Collaboration (HRC) that leverages Reinforcement Learning (RL) to optimize robot task assignments in real time. By training on a randomized set of assembly processes, the RL-GCN adapts to unpredictable human actions and heterogeneous assembly scenarios, significantly improving long-term assembly efficiency. Extensive simulations and real-world experiments validate that the framework enhances both production efficiency and flexibility, addressing critical challenges in modern industrial automation.
Real-time task assignment using a GCN-based framework can dramatically enhance assembly efficiency in unpredictable human-robot collaboration scenarios.
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.
