LAECIPS: Large vision model assisted adaptive edge–cloud collaboration for IoT-based embodied intelligence system

Embodied intelligence (EI) enables manufacturing systems to flexibly perceive, reason, adapt, and operate within dynamic shop floor environments. In smart manufacturing, a representative EI scenario is robotic visual inspection, where industrial robots must accurately inspect components on rapidly changing, heterogeneous production lines. This task requires both high inference accuracy — especially for uncommon defects — and low latency to match production speeds, despite evolving lighting, part geometries, and surface conditions. To meet these needs, we propose LAECIPS, a large vision model-assisted adaptive edge–cloud collaboration framework for IoT-based embodied intelligence systems. LAECIPS decouples large vision models in the cloud from lightweight models on the edge, enabling flexible model deployment and continual learning (automated model updates). Through identifying complex inspection cases, LAECIPS routes complex and uncertain inspection cases to the cloud while handling routine tasks at the edge, achieving both high accuracy and low latency. Experiments conducted on a real-world robotic semantic segmentation system for visual inspection demonstrate significant improvements in accuracy, processing latency, and communication overhead compared to state-of-the-art methods. From an industrial information integration perspective, LAECiPS operationalizes a complete edge–cloud information loop for smart manufacturing: integrating multi-source perception data at the edge, adaptively routing information to the cloud for large model assistance, and feeding back distilled knowledge to the edge for continual adaptation. This layered integration improves both accuracy and task-time-constrained latency, aligning with the focus on interoperable and scalable industrial information integration.