用AI让机器人自动插接线束连接器，提升精度与效率
AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
- 融合视觉触觉与力控，通过多模态Transformer优化插接策略
- 实验显示节拍时间显著缩短，插接成功率更高
- 无需专家深度学习知识，适合工业现场快速部署
尽管工业机器人已广泛应用于汽车装配，但线束安装仍主要依赖人工，因其需高精度与灵活性。为此，我们设计了一种基于AI的新型框架，通过结合力控与深度视觉触觉学习，实现电缆连接器自动插接。系统采用一阶优化方法，在多模态Transformer架构上训练，融合视觉、触觉与本体感知数据，优化搜索与插入策略。同时，构建了自动化数据采集与优化流程，降低对机器学习专业知识的依赖。该框架生成的机器人程序可直接在标准工业控制器上运行，支持人工审核与认证。在中控台装配任务上的实验验证表明，相比传统编程方法，节拍时间大幅缩短，鲁棒性显著提升。视频演示见 https://claudius-kienle.github.io/AppMuTT。
原文摘要 · Abstract (English)
Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, tactile, and proprioceptive data. Additionally, we design a novel automated data collection and optimization pipeline that minimizes the need for machine learning expertise. The framework optimizes robot programs that run natively on standard industrial controllers, permitting human experts to audit and certify them. Experimental validations on a center console assembly task demonstrate significant improvements in cycle times and robustness compared to conventional robot programming approaches. Videos are available under https://claudius-kienle.github.io/AppMuTT.
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