A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

[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 :

Computer science

Author, co-author :

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

External co-authors :

Language :

English

Title :

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Publication date :

10 July 2024

Journal title :

Robotics

ISSN :

2218-6581

Publisher :

MDPI AG

Special issue title :

Integrating Robotics into High-Accuracy Industrial Operations

Volume :

Issue :

Pages :

100

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.mdpi.com/2218-6581/13/7/100/pdf

FnR Project :

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

Funders :

Luxembourg National Research Fund

Funding number :

15882013

Funding text :

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.

Available on ORBilu :

since 19 July 2024

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