{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T23:10:03Z","timestamp":1771542603972,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819980697","type":"print"},{"value":"9789819980703","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8070-3_36","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T08:02:54Z","timestamp":1699948974000},"page":"471-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning Stable Nonlinear Dynamical System from\u00a0One Demonstration"],"prefix":"10.1007","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuze","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houcheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1146\/annurev-control-100819-063206","volume":"3","author":"H Ravichandar","year":"2020","unstructured":"Ravichandar, H., Polydoros, A.S., Chernova, S.: Recent advances in robot learning from demonstration. Ann. Rev. Control Robot. Auton. Syst. 3(1), 297\u2013330 (2020)","journal-title":"Ann. Rev. Control Robot. Auton. Syst."},{"issue":"2","key":"36_CR2","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/LRA.2022.3140677","volume":"7","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Cheng, L., Li, H., Cao, R.: Learning accurate and stable point-to-point motions: a dynamic system approach. IEEE Robot. Autom. Lett. 7(2), 1510\u20131517 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Liu, N., Lu, T., Y, Cai., Wang, R., Wang, S.: Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning. Sci. China Inf. Sci. 65, 114201 (2022)","DOI":"10.1007\/s11432-019-2648-7"},{"issue":"3","key":"36_CR4","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TRO.2016.2540623","volume":"32","author":"L Rozo","year":"2016","unstructured":"Rozo, L., Calinon, S., Caldwell, D.G., Jimenez, P., Torras, C.: Learning physical collaborative robot behaviors from human demonstrations. IEEE Trans. Robot. 32(3), 513\u2013527 (2016)","journal-title":"IEEE Trans. Robot."},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheng, L.: Online adaptive and attention-based reference path generation for upper-limb rehabilitation robot. In: China Automation Congress, pp. 5268\u20135273, Beijing, China (2021)","DOI":"10.1109\/CAC53003.2021.9728261"},{"issue":"5","key":"36_CR6","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1109\/TRO.2011.2159412","volume":"27","author":"SM Khansari-Zadeh","year":"2011","unstructured":"Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Trans. Robot. 27(5), 943\u2013957 (2011)","journal-title":"IEEE Trans. Robot."},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s11633-022-1346-z","volume":"20","author":"LH Kong","year":"2023","unstructured":"Kong, L.H., He, W., Chen, W.S., Zhang, H., Wang, Y.: Dynamic movement primitives based robot skills learning. Mach. Intell. Res. 20, 396\u2013407 (2023)","journal-title":"Mach. Intell. Res."},{"issue":"2","key":"36_CR8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1162\/NECO_a_00393","volume":"25","author":"AJ Ijspeert","year":"2013","unstructured":"Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328\u2013373 (2013)","journal-title":"Neural Comput."},{"key":"36_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103844","volume":"144","author":"M Ginesi","year":"2021","unstructured":"Ginesi, M., Sansonetto, N., Fiorini, P.: Overcoming some drawbacks of dynamic movement primitives. Robot. Auton. Syst. 144, 103844 (2021)","journal-title":"Robot. Auton. Syst."},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"2662","DOI":"10.1007\/s11431-021-1901-x","volume":"64","author":"C Li","year":"2021","unstructured":"Li, C., Li, Z., Jiang, Z., Cui, S., Liu, H., Cai, H.: Autonomous planning and control strategy for space manipulators with dynamics uncertainty based on learning from demonstrations. Sci. China Technol. Sci. 64, 2662\u20132675 (2021)","journal-title":"Sci. China Technol. Sci."},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.robot.2015.04.006","volume":"70","author":"K Neumann","year":"2015","unstructured":"Neumann, K., Steil, J.J.: Learning robot motions with stable dynamical systems under diffeomorphic transformations. Robot. Auton. Syst. 70, 1\u201315 (2015)","journal-title":"Robot. Auton. Syst."},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Neumann, K., Lemme, A., Steil, J.J.: Neural learning of stable dynamical systems based on data-driven Lyapunov candidates. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1216\u20131222, Tokyo, Japan (2013)","DOI":"10.1109\/IROS.2013.6696505"},{"issue":"6","key":"36_CR13","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.robot.2014.03.001","volume":"62","author":"SM Khansari-Zadeh","year":"2014","unstructured":"Khansari-Zadeh, S.M., Billard, A.: Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions. Robot. Auton. Syst. 62(6), 752\u2013765 (2014)","journal-title":"Robot. Auton. Syst."},{"key":"36_CR14","unstructured":"Rana, M.A., Li, A., Fox, D., Boots, B., Ramos, F., Ratliff, N.: Euclideanizing flows: diffeomorphic reduction for learning stable dynamical systems. In: 2th International Proceedings on the Conference on Learning for Dynamics and Control, pp. 630\u2013639, Online (2020)"},{"key":"36_CR15","doi-asserted-by":"publisher","first-page":"104312","DOI":"10.1016\/j.robot.2022.104312","volume":"160","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Cheng, L., Cao, R., Li, H., Yang, C.: A neural network based framework for variable impedance skills learning from demonstrations. Robot. Auton. Syst. 160, 104312 (2023)","journal-title":"Robot. Auton. Syst."},{"issue":"4","key":"36_CR16","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1109\/LRA.2023.3251190","volume":"8","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Cheng, L., Zhang, Y.: Learning robust point-to-point motions adversarially: a stochastic differential equation approach. IEEE Robot. Autom. Lett. 8(4), 2357\u20132364 (2023)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"6","key":"36_CR17","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.sysconle.2016.06.018","volume":"96","author":"N Perrin","year":"2016","unstructured":"Perrin, N., Schlehuber-Caissier, P.: Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems. Syst. Control Lett. 96(6), 51\u201359 (2016)","journal-title":"Syst. Control Lett."},{"key":"36_CR18","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning, 5th edn. Information Science and Statistics, Chapter 6, pp. 291\u2013294. Springer, New York (2006)"},{"key":"36_CR19","unstructured":"Homepage. https:\/\/bitbucket.org\/khansari\/lasahandwritingdataset\/. Accessed 25 Mar 2015"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Lam, S.K., Pitrou, A., Seibert, S.: Numba: a LLVM-based Python JIT compiler. In: International Proceedings on the Second Workshop on the LLVM Compiler Infrastructure in HPC, pp. 1\u20136, New York, USA (2015)","DOI":"10.1145\/2833157.2833162"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8070-3_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:27:17Z","timestamp":1710260837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8070-3_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"ISBN":["9789819980697","9789819980703"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8070-3_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"15 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}