歪みに頑健な自律ケーブル配線のためのロボット模倣学習
Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing
画像の歪みが発生するケーブル配線作業において、画像品質評価と信頼度に基づく学習機構を導入し、歪んだ観測下でも高性能を維持するロボット模倣学習フレームワークを提案した。
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著者: Hao Wang, Fu-Zhao Ou, Shiqi Wang, Zhaolin Wan, Xiaopeng Fan
分類: cs.RO
原文アブストラクト
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.
