[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122261-en":3,"doc-seo-122261-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122261,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A COMBINED MACHINE LEARNING APPROACH FOR THE ENGINEERING OF FLEXIBLE ASSEMBLY PROCESSES USING COLLABORATIVE ROBOTS - Dissertation Defence","The manufacturing industry is shifting rapidly from mass production to mass customization, driven by demand for highly personalized products. Collaborative robots (cobots) enable flexible high-mix, low-volume production, but conventional robot programming struggles in unstructured settings with frequent task variations. Learning from Demonstration (LfD) mitigates this by letting robots learn from human demonstrations, including by non-experts, yet real industrial deployment remains difficult while preserving high performance. This thesis proposes a roadmap for LfD-based mass customization, a one-shot framework (DFL-TORO) for time-optimal smooth trajectories, and a modular standardized LfD software framework validated through case studies and experiments.","PhD-FSTM-2025-028 Faculty of Science, Technology and Medicine  \nDISSERTATION  \nDefence held on 10 March 2025 in Luxembourg to obtain the degree of  \nDOCTEUR DE L’UNIVERSIT ´E DU LUXEMBOURG EN SCIENCES DE L’ING ´ENIEUR  \nby  \nAlireza BAREKATAIN  \nBorn on 22 October 1996 in Esfahan (Iran (Islamic Republic of))  \nA COMBINED MACHINE LEARNING APPROACH FOR THE ENGINEERING OF FLEXIBLE ASSEMBLY PROCESSES USING COLLABORATIVE ROBOTS  \nDissertation Defence Committee:  \nDr. Holger VOOS, Dissertation supervisor Professor, Universite´ du Luxembourg  \nDr. Olivier BRULS  \nProfessor, Universite´ de Lige  \nDr. Radu STATE, Chairman  \nProfessor, Universite´ du Luxembourg  \nDr. Carol MARTINEZ LUNA  \nResearch Scientist, Universite´ du Luxembourg  \nDr. Nico HOCHGESCHWENDER, Vice Chairman Professor, Universitt Bremen  \nAbstract  \nThe manufacturing industry is witnessing a rapid transformation from mass production to mass customization, driven by increasing consumer demand for highly personalized products. Collaborative robots (cobots) play a key role in enabling this shift, as their flexibility supports the dynamic and varied tasks necessary for producing high-mix, low-volume batches. However, traditional robot programming methods are not suited for unstructured environments with frequent task variations. These approaches quickly become cumbersome, prone to errors, and demand specialized robotic expertise. In response, Learning from Demonstration (LfD) has emerged as a promising paradigm for teaching tasks to robots by having them observe human demonstrations. This not only addresses the need for explicit programming but also allows non-experts to program robots efficiently. Nevertheless, practical deployment of LfD in real industrial scenarios remains challenging, particularly when ensuring that customized robotics solutions still meet the high-performance standards associated with traditional mass production processes. This thesis addresses these challenges by (1) proposing a practical roadmap that guides both researchers and industry practitioners in transitioning from rigid, mass production–oriented robotic tasks to flexible, LfD-based mass customization workflows; (2) introducing a one-shot demonstration framework, DFL-TORO, which captures time-optimal and smooth trajectories from a single human demonstration; and (3) presenting a modular, standardized software framework integrating LfD methodologies in manufacturing systems. Through an in-depth case study and experimental validations, the thesis lays the foundation for bridging the gap between academic research in LfDand its real-world adoption in mass customization settings.  \nIndex  \n1 Introduction 1  \n1.1 Research Motivation ................................ 1  \n1.2 Problem Statement and Research Goals ..................... 5  \n1.3 Publications ..................................... 7  \n1.4 Outline ........................................ 8  \n2 Background 9  \n2.1 Overview of Research Contributions (RCs) ................... 10  \n2.1.1 RC1: A Practical Roadmap ........................ 10  \n2.1.1.1 Background and Motivation ................... 10  \n2.1.1.2 State of the Art .......................... 11  \n2.1.1.3 Contribution ........................... 12  \n2.1.2 RC2: DFL-TORO .............................. 13  \n2.1.2.1 Background and Motivation ................... 13  \n2.1.2.2 State of the Art .......................... 15  \n2.1.2.3 Contribution ........................... 18  \n2.1.3 RC3: A Software Framework ....................... 19  \n2.1.3.1 background and Motivation ................... 19  \n2.1.3.2 State of the Art .......................... 20  \n2.1.3.3 Contribution ........................... 21  \n3 A Practical Roadmap to Learning from Demonstration for Robotic Manipulatorsin Manufacturing 23  \n3.1 What to Demonstrate ............................... 24  \n3.1.1 Full Task versus Subtask Demonstration ................. 26  \n3.1.2 Motion-Based versus Contact-Based Demonstration .......... 29 ","cbCaitBFh8QG52lF","https://ap.wps.com/l/cbCaitBFh8QG52lF","pdf",20997518,1,161,"English","en",105,"# Index\n## 1 Introduction\n## 2 Background\n## 3 A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing\n## 4 DFL-TORO: A Demonstration Framework for Learning Time-Optimal Robotic Tasks via One-shot Kinesthetic Demonstration","[{\"question\":\"Why are collaborative robots important for flexible assembly processes?\",\"answer\":\"Collaborative robots support flexible and dynamic tasks, enabling high-mix, low-volume batches needed for mass customization.\"},{\"question\":\"What challenge does Learning from Demonstration (LfD) address in robot programming?\",\"answer\":\"LfD replaces rigid explicit programming by learning tasks from human demonstrations, which can also reduce the need for specialized robotic expertise.\"},{\"question\":\"What contributions does the thesis make to enable real industrial deployment of LfD?\",\"answer\":\"It provides a practical roadmap for transitioning to LfD-based workflows, introduces the one-shot DFL-TORO framework for time-optimal smooth trajectories, and proposes a modular standardized software framework for integrating LfD methods.\"}]","A COMBINED MACHINE LEARNING APPROACH FOR THE ENGINEERING OF FLEXIBLE ASSEMBLY PROCESSES USING COLLABORATIVE ROBOTS - 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