Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing

2026-06-09

AI Summary

Severe image observation distortions significantly hinder the performance of imitation learning in robotic autonomous cable routing tasks. To address this challenge, this work proposes a distortion-robust imitation learning framework that explicitly integrates image quality assessment into the learning process for the first time. The framework employs a confidence-weighted mechanism to adaptively emphasize hard examples and incorporates a joint decision module that unifies discrete skills with continuous actions. This approach substantially enhances model robustness under low-quality visual inputs, improves decision accuracy, and increases overall task stability.

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.

Problem

• image distortion
• robotic imitation learning
• cable routing
• image quality assessment
• autonomous control

Innovation

• distortion-resilient
• imitation learning
• image quality assessment
• confidence-based learning
• autonomous cable routing