用学习增强的机器人系统实现产线可靠自动化，无需物理围栏
Learning-augmented robotic automation for real-world manufacturing
- 融合学习控制与神经3D安全监控，结合传统工业流程
- 5小时10分钟连续运行，108台电机零缺陷，合格率达99.4%
- 适合想在真实产线部署自适应机器人的制造企业
工业机器人广泛应用于制造，但多数操作仍依赖对环境变化敏感的固定路径脚本。基于学习的控制虽更具适应性，但在实际产线中能否持续可靠运行、保证质量并确保人员安全尚不明确。本文提出学习增强型机器人自动化系统，将学习任务控制器与神经3D安全监控集成至传统工业工作流中。我们在电动机产线部署该系统，实现了在真实制造约束下的柔性电缆插入与焊接自动化，此前由人工完成。每项任务仅需不到20分钟真实数据，系统连续运行5小时10分钟，生产108台电机，无物理围栏，产品级质检通过率达99.4%。系统保持接近人工节拍，同时降低焊点质量与周期时间的波动。结果证明了学习方法在工业自动化中的实用路径。
原文摘要 · Abstract (English)
Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes. Learning-based control offers a more adaptive alternative, but it remains unclear whether such methods, still mostly confined to laboratory demonstrations, can sustain hours of reliable operation, deliver consistent quality, and behave safely around people on a live production line. Here we present Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows. We deployed the system on an electric-motor production line to automate deformable cable insertion and soldering under real manufacturing constraints, a step previously performed manually by human workers. With less than 20 min of real-world data per task, the system operated continuously for 5 h 10 min, producing 108 motors without physical fencing and achieving a 99.4% pass rate on product-level quality-control tests. It maintained near-human takt time while reducing variability in solder-joint quality and cycle time. These results establish a practical pathway for extending industrial automation with learning-based methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。