Barekatain, A.; Habibi, H.; Voos, H. A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing. Robotics2024, 13, 100.
Barekatain, A.; Habibi, H.; Voos, H. A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing. Robotics 2024, 13, 100.
Barekatain, A.; Habibi, H.; Voos, H. A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing. Robotics2024, 13, 100.
Barekatain, A.; Habibi, H.; Voos, H. A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing. Robotics 2024, 13, 100.
Abstract
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.
Keywords
Learning from Demonstration; Robot Learning; Robotic Manipulators; Manufacturing Robotics
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
