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.

本研究解决了工业机器人在汽车制造中线束安装仍主要依赖人工的问题，提出了一种创新的基于人工智能的框架，通过结合力控制和深度视觉触觉学习，实现连接器的自动配合。实验结果表明，该框架显著提高了周期时间和鲁棒性，具有重要的潜在应用价值。

基于人工智能的坚固模型驱动连接器配合框架在机器人线束安装中的应用