{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T10:17:20Z","timestamp":1785493040454,"version":"3.56.0"},"reference-count":108,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tecnologico de Monterrey"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Human\u2013Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill\/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.<\/jats:p>","DOI":"10.3390\/robotics11060126","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:24:53Z","timestamp":1668565493000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Learning from Demonstrations in Human\u2013Robot Collaborative Scenarios: A Survey"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2429-6347","authenticated-orcid":false,"given":"Arturo Daniel","family":"Sosa-Ceron","sequence":"first","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6495-9980","authenticated-orcid":false,"given":"Hugo Gustavo","family":"Gonzalez-Hernandez","sequence":"additional","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2797-9381","authenticated-orcid":false,"given":"Jorge Antonio","family":"Reyes-Avenda\u00f1o","sequence":"additional","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","first-page":"899","article-title":"Scanning the industry 4.0: A literature review on technologies for manufacturing systems","volume":"22","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1080\/00207543.2018.1510558","article-title":"An overview of current technologies and emerging trends in factory automation","volume":"57","author":"Dotoli","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1177\/0954405417736547","article-title":"Smart manufacturing: Characteristics, technologies and enabling factors","volume":"233","author":"Mittal","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part B J. Eng. Manuf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1142\/S0219843608001303","article-title":"Human\u2013robot collaboration: A survey","volume":"5","author":"Bauer","year":"2008","journal-title":"Int. J. Humanoid Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s43154-020-00006-5","article-title":"Trends in smart manufacturing: Role of humans and industrial robots in smart factories","volume":"1","author":"Evjemo","year":"2020","journal-title":"Curr. Robot. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.mechatronics.2018.02.009","article-title":"Survey on human\u2013robot collaboration in industrial settings: Safety, intuitive interfaces and applications","volume":"55","author":"Villani","year":"2018","journal-title":"Mechatronics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102022","DOI":"10.1016\/j.rcim.2020.102022","article-title":"Safety assurance mechanisms of collaborative robotic systems in manufacturing","volume":"67","author":"Bi","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ergon.2016.11.011","article-title":"Human-oriented design of collaborative robots","volume":"57","author":"Maurice","year":"2016","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101998","DOI":"10.1016\/j.rcim.2020.101998","article-title":"Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review","volume":"67","author":"Gualtieri","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1080\/01691864.2019.1636714","article-title":"Human\u2013robot interaction in industrial collaborative robotics: A literature review of the decade 2008\u20132017","volume":"33","author":"Hentout","year":"2019","journal-title":"Adv. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.robot.2019.03.003","article-title":"Cobot programming for collaborative industrial tasks: An overview","volume":"116","author":"Zaatari","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Michaelis, J.E., Siebert-Evenstone, A., Shaffer, D.W., and Mutlu, B. (2020, January 25\u201330). Collaborative or Simply Uncaged? Understanding Human-Cobot Interactions in Automation. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3313831.3376547"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.robot.2008.10.024","article-title":"A survey of robot learning from demonstration","volume":"57","author":"Argall","year":"2009","journal-title":"Robot. Auton. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3054912","article-title":"Imitation learning: A survey of learning methods","volume":"50","author":"Hussein","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhu, Z., and Hu, H. (2018). Robot Learning from Demonstration in Robotic Assembly: A Survey. Robot, 7.","DOI":"10.3390\/robotics7020017"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1146\/annurev-control-100819-063206","article-title":"Recent Advances in Robot Learning from Demonstration","volume":"3","author":"Ravichandar","year":"2020","journal-title":"Annu. Rev. Control. Robot. Auton. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1007\/s11431-020-1648-4","article-title":"Robot learning from demonstration for path planning: A review","volume":"63","author":"Xie","year":"2020","journal-title":"Sci. China Technol. Sci."},{"key":"ref_18","unstructured":"Kitchenham, B. (2004). Procedures for Performing Systematic Reviews, Keele University."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2014A systematic literature review","volume":"51","author":"Kitchenham","year":"2008","journal-title":"Inf. Softw. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1177\/0739456X17723971","article-title":"Guidance on conducting a systematic literature review","volume":"39","author":"Xiao","year":"2019","journal-title":"J. Plan. Educ. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Scells, H., and Zuccon, G. (2018, January 8\u201312). Generating better queries for systematic reviews. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3210020"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1177\/0894439309332293","article-title":"Supporting systematic reviews using text mining","volume":"27","author":"Ananiadou","year":"2009","journal-title":"Soc. Sci. Comput. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/2046-4053-3-74","article-title":"Systematic review automation technologies","volume":"3","author":"Tsafnat","year":"2014","journal-title":"Syst. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Billard, A.G., Calinon, S., and Dillmann, R. (2016). Learning from Humans. Springer Handbook of Robotics, Springer.","DOI":"10.1007\/978-3-319-32552-1_74"},{"key":"ref_25","first-page":"1","article-title":"Robot learning from human teachers","volume":"28","author":"Chernova","year":"2014","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s43154-020-00023-4","article-title":"Advanced Robot Programming: A Review","volume":"1","author":"Zhou","year":"2020","journal-title":"Curr. Robot. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Koskinopoulou, M., Piperakis, S., and Trahanias, P. (2016, January 7\u201310). Learning from demonstration facilitates human-robot collaborative task execution. Proceedings of the 2016 11th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, New Zealand.","DOI":"10.1109\/HRI.2016.7451734"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rcim.2018.12.017","article-title":"Human-like coordination motion learning for a redundant dual-arm robot","volume":"57","author":"Qu","year":"2019","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, Y., Li, R., and Jia, Y. (2019). Learning and comfort in human-robot interaction: A review. Appl. Sci., 9.","DOI":"10.3390\/app9235152"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lopes, M., Melo, F., and Montesano, L. (2009). Active Learning for Reward Estimation in Inverse Reinforcement Learning, Springer.","DOI":"10.1007\/978-3-642-04174-7_3"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TASE.2018.2840345","article-title":"Facilitating Human-Robot Collaborative Tasks by Teaching-Learning-Collaboration from Human Demonstrations","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_32","first-page":"958","article-title":"Learning rhythmic movements by demonstration using nonlinear oscillators","volume":"1","author":"Ijspeert","year":"2002","journal-title":"IEEE Int. Conf. Intell. Robot. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fink, G.A. (2014). Markov Models for Pattern Recognition, Springer.","DOI":"10.1007\/978-1-4471-6308-4"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Parsons, O.E. (2020). A Gaussian Mixture Model Approach to Classifying Response Types, Springer.","DOI":"10.1007\/978-3-030-23876-6_1"},{"key":"ref_36","unstructured":"Cowan, J., Tesauro, G., and Alspector, J. (December, January 30). Supervised learning from incomplete data via an EM approach. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.21105\/joss.03054","article-title":"gmr: Gaussian Mixture Regression","volume":"6","author":"Fabisch","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_38","unstructured":"Odom, P., and Natarajan, S. (2016, January 9\u201313). Active Advice Seeking for Inverse Reinforcement Learning. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, Singapore."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nehaniv, C.L., and Dautenhahn, K. (2007). Task learning through imitation and human\u2013robot interaction. Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, Cambridge University Press.","DOI":"10.1017\/CBO9780511489808"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Luo, Y., Yin, L., Bai, W., and Mao, K. (2020). An Appraisal of Incremental Learning Methods. Entropy, 22.","DOI":"10.3390\/e22111190"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., and Peters, J. (2016, January 15\u201317). Incremental imitation learning of context-dependent motor skills. Proceedings of the 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico.","DOI":"10.1109\/HUMANOIDS.2016.7803300"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nozari, S., Krayani, A., Marcenaro, L., Martin, D., and Regazzoni, C. (September, January 29). Incremental Learning through Probabilistic Behavior Prediction. Proceedings of the 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia.","DOI":"10.23919\/EUSIPCO55093.2022.9909735"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6052","DOI":"10.1109\/LRA.2022.3165531","article-title":"Learning to Pick at Non-Zero-Velocity From Interactive Demonstrations","volume":"7","author":"Franzese","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1177\/0278364913495721","article-title":"Reinforcement learning in robotics: A survey","volume":"32","author":"Kober","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"126","DOI":"10.3389\/frobt.2018.00126","article-title":"Toward an interactive reinforcement based learning framework for human robot collaborative assembly processes","volume":"5","author":"Akkaladevi","year":"2018","journal-title":"Front. Robot. AI"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Winter, J.D., Beir, A.D., Makrini, I.E., de Perre, G.V., Now\u00e9, A., and Vanderborght, B. (2019). Accelerating interactive reinforcement learning by human advice for an assembly task by a cobot. Robotics, 8.","DOI":"10.3390\/robotics8040104"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10514-021-10030-9","article-title":"User intent estimation during robot learning using physical human robot interaction primitives","volume":"46","author":"Lai","year":"2022","journal-title":"Auton. Robot."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6775","DOI":"10.1007\/s00170-022-08652-z","article-title":"A robot learning from demonstration framework for skillful small parts assembly","volume":"119","author":"Hu","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6807","DOI":"10.1007\/s00170-022-09177-1","article-title":"Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing","volume":"120","author":"Zhang","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wang, Y., Hu, K., and Li, W. (2021). Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing. J. Intell. Manuf., 1\u201319.","DOI":"10.1007\/s10845-021-01825-9"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, L., Jia, S., Wang, G., Turner, A., and Ratchev, S. (2021). Enhancing learning capabilities of movement primitives under distributed probabilistic framework for flexible assembly tasks. Neural Comput. Appl., 1\u201312.","DOI":"10.1109\/SMC42975.2020.9283066"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103474","DOI":"10.1016\/j.robot.2020.103474","article-title":"Learning robots to grasp by demonstration","volume":"127","author":"Coninck","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1109\/LRA.2019.2928782","article-title":"Intuitive Task-Level Programming by Demonstration Through Semantic Skill Recognition","volume":"4","author":"Steinmetz","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_54","first-page":"422","article-title":"Advanced Robotics Towards robot cell matrices for agile production-SDU Robotics\u2019 assembly cell at the WRC 2018 Towards robot cell matrices for agile production-SDU Robotics\u2019 assembly cell at the WRC 2018","volume":"2020","author":"Schlette","year":"2019","journal-title":"Adv. Robot."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10514-018-9725-6","article-title":"Robot learning of industrial assembly task via human demonstrations","volume":"43","author":"Kyrarini","year":"2019","journal-title":"Auton. Robot."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s10514-017-9680-7","article-title":"Co-manipulation with a library of virtual guiding fixtures","volume":"42","author":"Raiola","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.robot.2017.12.001","article-title":"Robot learning from demonstrations: Emulation learning in environments with moving obstacles","volume":"101","author":"Esfahani","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3389\/frobt.2016.00030","article-title":"Learning Controllers for Reactive and Proactive Behaviors in Human\u2013Robot Collaboration","volume":"3","author":"Rozo","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TRO.2016.2540623","article-title":"Learning Physical Collaborative Robot Behaviors From Human Demonstrations","volume":"32","author":"Rozo","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"354","DOI":"10.3389\/frobt.2021.767878","article-title":"Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration","volume":"8","author":"Iturrate","year":"2021","journal-title":"Front. Robot. AI"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"102169","DOI":"10.1016\/j.rcim.2021.102169","article-title":"Optimised Learning from Demonstrations for Collaborative Robots","volume":"71","author":"Wang","year":"2021","journal-title":"Robot.-Comput.-Integr. Manuf."},{"key":"ref_62","unstructured":"Liang, Y.S., Pellier, D., Fiorino, H., and Pesty, S. (September, January 28). Evaluation of a Robot Programming Framework for Non-Experts Using Symbolic Planning Representations. Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s13218-019-00582-5","article-title":"A Semantic-Based Method for Teaching Industrial Robots New Tasks","volume":"33","author":"Bergner","year":"2019","journal-title":"KI-Kunstl. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"133762","DOI":"10.1109\/ACCESS.2021.3115756","article-title":"Adaptive multi-task human-robot interaction based on human behavioral intention","volume":"9","author":"Fu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s12369-021-00775-9","article-title":"iRoPro: An interactive Robot Programming Framework","volume":"14","author":"Liang","year":"2022","journal-title":"Int. J. Soc. Robot."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rcim.2018.03.008","article-title":"Skill-based instruction of collaborative robots in industrial settings","volume":"53","author":"Schou","year":"2018","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TCDS.2020.2968056","article-title":"A Framework of Hybrid Force\/Motion Skills Learning for Robots","volume":"13","author":"Wang","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.promfg.2017.07.221","article-title":"Teaching Assembly by Demonstration Using Advanced Human Robot Interaction and a Knowledge Integration Framework","volume":"11","author":"Haage","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"103935","DOI":"10.1016\/j.robot.2021.103935","article-title":"An adaptive learning and control framework based on dynamic movement primitives with application to human-robot handovers","volume":"148","author":"Wu","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/TSMC.2020.3005340","article-title":"Learn How to Assist Humans Through Human Teaching and Robot Learning in Human-Robot Collaborative Assembly","volume":"52","author":"Sun","year":"2022","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1109\/TII.2017.2773479","article-title":"A Multirobot Cooperation Framework for Sewing Personalized Stent Grafts","volume":"14","author":"Huang","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1729881417738884","DOI":"10.1177\/1729881417738884","article-title":"A machine learning-based visual servoing approach for fast robot control in industrial setting","volume":"14","author":"Castelli","year":"2017","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/s40436-020-00303-4","article-title":"Robot programming by demonstration: A novel system for robot trajectory programming based on robot operating system","volume":"8","author":"Zhang","year":"2020","journal-title":"Adv. Manuf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1007\/s10845-021-01743-w","article-title":"An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing","volume":"33","author":"Zaatari","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"102109","DOI":"10.1016\/j.rcim.2020.102109","article-title":"iTP-LfD: Improved task parametrised learning from demonstration for adaptive path generation of cobot","volume":"69","author":"Zaatari","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"82351","DOI":"10.1109\/ACCESS.2021.3086701","article-title":"An Approach to Acquire Path-Following Skills by Industrial Robots from Human Demonstration","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1145\/3319502.3374784","article-title":"Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization","volume":"10","author":"Racca","year":"2020","journal-title":"HRI ACM\/IEEE Int. Conf. Hum.-Robot Interact."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1007\/s10514-017-9678-1","article-title":"Robot adaptation to human physical fatigue in human\u2013robot co-manipulation","volume":"42","author":"Peternel","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Al-Yacoub, A., Zhao, Y.C., Eaton, W., Goh, Y.M., and Lohse, N. (2021). Improving human robot collaboration through Force\/Torque based learning for object manipulation. Robot. Comput.-Integr. Manuf., 69.","DOI":"10.1016\/j.rcim.2020.102111"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"145604","DOI":"10.1109\/ACCESS.2019.2945484","article-title":"Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration","volume":"7","author":"Zeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Soares, I., Petry, M., and Moreira, A.P. (2021). Programming Robots by Demonstration Using Augmented Reality. Sensors, 21.","DOI":"10.3390\/s21175976"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"102833","DOI":"10.1016\/j.mechatronics.2022.102833","article-title":"Control framework for collaborative robot using imitation learning-based teleoperation from human digital twin to robot digital twin","volume":"85","author":"Lee","year":"2022","journal-title":"Mechatronics"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2050001","DOI":"10.1142\/S0219843620500012","article-title":"Incremental Learning of an Open-Ended Collaborative Skill Library","volume":"17","author":"Koert","year":"2020","journal-title":"Int. J. Humanoid Robot."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"104046","DOI":"10.1016\/j.robot.2022.104046","article-title":"Environment-adaptive learning from demonstration for proactive assistance in human\u2013robot collaborative tasks","volume":"151","author":"Qian","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Tang, T., Lin, H.C., Zhao, Y., Fan, Y., Chen, W., and Tomizuka, M. (2016, January 12\u201315). Teach industrial robots peg-hole-insertion by human demonstration. Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Banff, AB, Canada.","DOI":"10.1109\/AIM.2016.7576815"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/11008941_60","article-title":"Learning movement primitives","volume":"15","author":"Schaal","year":"2005","journal-title":"Springer Tracts Adv. Robot."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Hoffmann, H., Pastor, P., Park, D.H., and Schaal, S. (2009, January 12\u201317). Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152423"},{"key":"ref_88","unstructured":"Paraschos, A., Daniel, C., Peters, J., and Neumann, G. (2013, January 5\u201310). Probabilistic movement primitives. Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), Lake Tahoe, NV, USA."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s10514-017-9648-7","article-title":"Using probabilistic movement primitives in robotics","volume":"42","author":"Paraschos","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s10514-016-9556-2","article-title":"Probabilistic movement primitives for coordination of multiple human\u2013robot collaborative tasks","volume":"41","author":"Maeda","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Koert, D., Trick, S., Ewerton, M., Lutter, M., and Peters, J. (2018, January 6\u20139). Online Learning of an Open-Ended Skill Library for Collaborative Tasks. Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China.","DOI":"10.1109\/HUMANOIDS.2018.8625031"},{"key":"ref_92","unstructured":"Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., and Peters, J. (June, January 31). Interaction primitives for human-robot cooperation tasks. Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TSMCB.2006.886952","article-title":"On learning, representing, and generalizing a task in a humanoid robot","volume":"37","author":"Calinon","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MRA.2010.936947","article-title":"Learning and reproduction of gestures by imitation","volume":"17","author":"Calinon","year":"2010","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s12369-019-00548-5","article-title":"Can Human-Inspired Learning Behaviour Facilitate Human\u2014Robot Interaction?","volume":"12","author":"Villalobos","year":"2020","journal-title":"Int. J. Soc. Robot."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Carmigniani, J., and Furht, B. (2011). Augmented reality: An overview. Handbook of Augmented Reality, Springer.","DOI":"10.1007\/978-1-4614-0064-6_1"},{"key":"ref_97","unstructured":"Sherman, W.R., and Craig, A.B. (2003). Understanding Virtual Reality, Morgan Kauffman."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Grieves, M., and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems, Springer.","DOI":"10.1007\/978-3-319-38756-7_4"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Palmarini, R., Amo, I.F.D., Bertolino, G., Dini, G., Erkoyuncu, J.A., Roy, R., and Farnsworth, M. (2018, January 23\u201325). Designing an AR interface to improve trust in Human-Robots collaboration. Proceedings of the 28th CIRP Design Conference, Nantes, France.","DOI":"10.1016\/j.procir.2018.01.009"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Shu, B., Sziebig, G., and Pieters, R. (2019, January 12\u201314). Architecture for Safe Human-Robot Collaboration: Multi-Modal Communication in Virtual Reality for Efficient Task Execution. Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada.","DOI":"10.1109\/ISIE.2019.8781372"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Materna, Z., Kapinus, M., Beran, V., Smr\u017e, P., and Zem\u010d\u00edk, P. (2018, January 27\u201331). Interactive Spatial Augmented Reality in Collaborative Robot Programming: User Experience Evaluation. Proceedings of the 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Nanjing, China.","DOI":"10.1109\/ROMAN.2018.8525662"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Bambussek, D., Materna, Z.Z., Kapinus, M., Beran, V.V., Smrz, P., Bambu\u0161ek, D., Materna, Z.Z., Kapinus, M., Beran, V.V., and Smr\u017e, P. (2019, January 14\u201319). Combining Interactive Spatial Augmented Reality with Head-Mounted Display for End-User Collaborative Robot Programming. Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956315"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s12008-018-0516-2","article-title":"Off-line programming of an industrial robot in a virtual reality environment","volume":"13","author":"Manou","year":"2019","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Burghardt, A., Szybicki, D., Gierlak, P., Kurc, K., Pietru\u015b, P., and Cygan, R. (2020). Programming of Industrial Robots Using Virtual Reality and Digital Twins. Appl. Sci., 10.","DOI":"10.3390\/app10020486"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"102092","DOI":"10.1016\/j.rcim.2020.102092","article-title":"Digital twins for collaborative robots: A case study in human-robot interaction","volume":"68","author":"Malik","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, L., Rodr\u00edguez-Jim\u00e9nez, S., Rodr\u00edguez, N., Usamentiaga, R., and Garc\u00eda, D.F. (2020). Digital twin and virtual reality based methodology for multi-robot manufacturing cell commissioning. Appl. Sci., 10.","DOI":"10.3390\/app10103633"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jmsy.2020.06.012","article-title":"A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence","volume":"58","author":"Xia","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rcim.2015.04.002","article-title":"Robot skills for manufacturing: From concept to industrial deployment","volume":"37","author":"Pedersen","year":"2016","journal-title":"Robot. Comput.-Integr. 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