# AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation

arXiv: https://arxiv.org/abs/2503.09409 · HTML: https://mlsanity.com/paper/2503.09409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [A Differentiable Physics Engine for Deep Learning in Robotics](https://mlsanity.com/paper/1611.01652/) (2016)
- [Scalable Differentiable Physics for Learning and Control](https://mlsanity.com/paper/2007.02168/) (2020)
- [Robot Program Parameter Inference via Differentiable Shadow Program Inversion](https://mlsanity.com/paper/2103.14452/) (2021)
- [Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments](https://mlsanity.com/paper/2207.07524/) (2022)
- [Practical First-Order Bayesian Optimization Algorithms](https://mlsanity.com/paper/2306.10815/) (2023)
- [A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly](https://mlsanity.com/paper/2309.13744/) (2023)
- [Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses](https://mlsanity.com/paper/2309.13746/) (2023)
- [MuTT: A Multimodal Trajectory Transformer for Robot Skills](https://mlsanity.com/paper/2407.15660/) (2024)
- [A Closed-Loop Bin Picking System for Entangled Wire Harnesses using Bimanual and Dynamic Manipulation](https://mlsanity.com/paper/2306.14595/) (2023)
- [BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming](https://mlsanity.com/paper/2404.13652/) (2024)
- [Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization](https://mlsanity.com/paper/2404.19349/) (2024)
- [Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization](https://mlsanity.com/paper/2409.08678/) (2024)

## Abstract (arXiv)

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.