COACH: Adaptive Robust Human-Robot Collaboration for Efficient Smart Manufacturing

Modern smart manufacturing pipelines have pervasively collaborated human workers, mobile robots, and industrial Internet of Things (IIoT) in shared workspaces for versatile production tasks. Despite the promising capacity of individual entities, the performance of these IIoT systems largely relies on pipeline coordination, i.e., task dispatching between humans and robots, which is particularly challenging under heterogeneous physical constraints and complex environmental uncertainties. Nonetheless, existing works either rely on traditional operation frameworks that lack scalability for large-scale complex production, or propose customized solutions for fixed agent models, overlooking the evolving nature of IIoT environments. To address these limitations, this paper proposes COACH, a human-robot collaborative manufacturing system that enables robust constraint-aware coordination across humans, robots, and IIoT. Specifically, COACH designs a scalable contextual encoder to represent the evolving relationships among human and robot agents in dynamic heterogeneous graphs. With that, a novel experience-driven task dispatcher is developed, enabling both high-performance and computation-efficient policy generation concerning the status of IIoT. To accommodate changing human fatigue and pipeline scales, COACH further develops a curriculum-enhanced reinforcement learning module for efficient dispatcher adaptation. Extensive evaluations using both synthetic testbeds and real-world manufacturing datasets demonstrate that COACH improves the feasible ratio of manufacturing pipelines by up to 27.4% and achieves up to 13.9% improvement in time efficiency compared to competing baselines across diverse job scales and environmental settings.