製造業におけるロボットマニピュレータのための模倣学習実践ロードマップ
A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing
製造現場の産業用マニピュレータに模倣学習(LfD)を導入するための、実践者向けの質問形式ロードマップを提示した論文。
著者: Alireza Barekatain, Hamed Habibi, Holger Voos
分類: cs.RO
原文アブストラクト
This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD ) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.
