# Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing Source: [Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing]() Collection index: [llms.txt]() Data updated: 2026-06-12T05:00:05.419Z Authors: Hao Wang, Fu-Zhao Ou, Shiqi Wang, Zhaolin Wan, Xiaopeng Fan Published: 2026-06-10T02:07:59.000Z ## Abstract The rapid development of intelligent control methodologies has endowed robots with powerful autonomous intelligence. Cable routing, a ubiquitous foundational task in industry, provides a rigorous benchmark for robotic dexterity and sequential decision-making. In these practical scenarios, image observation distortion frequently occurs. Samples characterized by low-quality image observations often hinder accurate model training, posing challenges to the reliability and accuracy of intelligent control systems. Nevertheless, no dedicated intelligent control solution has been proposed for scenarios of image signal distortion. Meanwhile, image quality information has not been sufficiently exploited to further enhance the performance of intelligent control methodologies. To this end, we propose a novel robotic imitation learning framework that comprises an image quality assessment module, a confidence-based learning mechanism, and a decision-making module, which is designed to maintain high performance even under distorted image observations. In the proposed framework, the image quality assessment module synergizes with the confidence-based learning mechanism to enhance the efficacy of the decision-making module. Specifically, the image quality assessment module is incorporated to extract image quality information from image observations, while the confidence-based learning mechanism adaptively prioritizes challenging samples to improve learning effectiveness. The decision-making module determines appropriate discrete skills or continuous actions. Experimental results demonstrate that our formulated framework enhances the overall performance of the decision-making module. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [High-level skill selection on MyRMB high-level (test)](): High-level skill selection; MyRMB high-level (test); PLCC (Pearson Correlation): 0.95. - [Low-level routing on MyRMB low-level (test)](): Low-level routing; MyRMB low-level (test); PLCC: 0.9023. ## Original sources and implementation [Original paper]() [PDF]()