LAECIPS：面向物联网具身智能系统的大视觉模型辅助自适应边缘-云协作
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Embodied Intelligence System
AI总结：
针对智能制造中机器人视觉检测对高精度和低延迟的需求，提出大视觉模型辅助的自适应边缘-云协作框架LAECIPS，通过解耦云边模型和困难输入挖掘策略，在真实系统上显著提升精度并降低延迟与通信开销。
摘要：
具身智能（EI）使制造系统能够在动态车间环境中灵活地感知、推理、适应和操作。在智能制造中，一个典型的具身智能场景是机器人视觉检测，其中工业机器人必须在快速变化、异构的生产线上准确检测组件。该任务既需要高推理精度（尤其是对于罕见缺陷），又需要低延迟以匹配生产速度，尽管光照、零件几何形状和表面条件不断变化。为满足这些需求，我们提出了LAECIPS，一种用于基于物联网的具身智能系统的大视觉模型辅助自适应边缘-云协作框架。LAECIPS将云中的大视觉模型与边缘的轻量级模型解耦，实现即插即用的模型适应和持续学习。通过基于困难输入挖掘的推理策略，LAECIPS将复杂和不确定的检测案例路由到云端，同时在边缘处理常规任务，从而实现高精度和低延迟。在真实的机器人语义分割系统上进行的视觉检测实验表明，与最先进的方法相比，在精度、处理延迟和通信开销方面均有显著改进。LAECIPS为智能制造中的具身智能提供了实用且可扩展的基础，特别是在自适应机器人检测和质量控制场景中。
英文摘要：
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 plug-and-play model adaptation and continual learning. Through a hard input mining-based inference strategy, 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. LAECIPS provides a practical and scalable foundation for embodied intelligence in smart manufacturing, especially in adaptive robotic inspection and quality control scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
