AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
This work addresses a domain-specific problem in robotic automation for automotive manufacturing, offering an incremental advancement by optimizing existing industrial processes.
The paper tackles the challenge of automating wire harness installation in automotive assembly by developing an AI-based framework that integrates force control with deep visuotactile learning, resulting in significant improvements in cycle times and robustness compared to conventional methods.
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.