[en] 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. This paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, this paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BAREKATAIN, Alireza ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
HABIBI, Hamed ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
VOOS, Holger ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing
Date de publication/diffusion :
10 juillet 2024
Titre du périodique :
Robotics
ISSN :
2218-6581
Maison d'édition :
MDPI AG
Titre particulier du numéro :
Integrating Robotics into High-Accuracy Industrial Operations
FNR15882013 - A Combined Machine Learning Approach For The Engineering Of Flexible Assembly Processes Using Collaborative Robots, 2021 (01/04/2021-28/02/2025) - Alireza Barekatain
Organisme subsidiant :
Luxembourg National Research Fund
N° du Fonds :
15882013
Subventionnement (détails) :
This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), ML-COBOTS Project, ref. 15882013. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the authors have applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any author-accepted manuscript version arising from this submission.
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