{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:08:14Z","timestamp":1759385294692,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2017YFB1300202"],"award-info":[{"award-number":["2017YFB1300202"]}]},{"name":"NSFC grants","award":["U16132132","61375005"],"award-info":[{"award-number":["U16132132","61375005"]}]},{"name":"NSFC grants","award":["61503383","61210009"],"award-info":[{"award-number":["61503383","61210009"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Science","award":["XDB32050100"],"award-info":[{"award-number":["XDB32050100"]}]},{"name":"Dongguan core technology research frontier project","award":["2019622101001"],"award-info":[{"award-number":["2019622101001"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20181189"],"award-info":[{"award-number":["BK20181189"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Strategic Priority Research Program of the CAS","award":["XDB02080003"],"award-info":[{"award-number":["XDB02080003"]}]},{"name":"Key Program Special Fund in XJTLU","award":["KSF-A-01","KSF-E-26"],"award-info":[{"award-number":["KSF-A-01","KSF-E-26"]}]},{"name":"Key Program Special Fund in XJTLU","award":["KSF-P-02"],"award-info":[{"award-number":["KSF-P-02"]}]},{"name":"NSFC grants","award":["61876155"],"award-info":[{"award-number":["61876155"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s12559-020-09784-8","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T16:06:28Z","timestamp":1604937988000},"page":"394-402","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["State Primitive Learning to Overcome Catastrophic Forgetting in Robotics"],"prefix":"10.1007","volume":"13","author":[{"given":"Fangzhou","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"issue":"1","key":"9784_CR1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R. Multitask learning. Machine learning. 1997;28(1):41\u201375.","journal-title":"Multitask learning. Machine learning"},{"issue":"4","key":"9784_CR2","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French RM. Catastrophic forgetting in connectionist networks. Trends Cogn Sci. 1999;3(4):128\u201335.","journal-title":"Trends Cogn Sci."},{"key":"9784_CR3","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014. p. 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"9784_CR4","unstructured":"Gupta A, Devin C, Liu Y, Abbeel P, Levine, Learning invariant feature spaces to transfer skills with reinforcement learning. Proceedings of the International Conference on Learning Representations. (ICLR). 2017."},{"issue":"1","key":"9784_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1038\/s41524-017-0055-6","volume":"3","author":"E Kim","year":"2017","unstructured":"Kim E, Huang K, Jegelka S, Olivetti E. Virtual screening of inorganic materials synthesis parameters with deep learning. Npj Comput Mater. 2017;3(1):53.","journal-title":"Npj Comput. Mater"},{"key":"9784_CR6","unstructured":"Kingma DP, Ba J. Adam. A method for stochastic optimization 2015."},{"key":"9784_CR7","unstructured":"Kingma DP, Welling M. Auto-encoding variational bayes. In: Proceedings of the International Conference on Learning Representations (ICLR) 2014."},{"key":"9784_CR8","doi-asserted-by":"crossref","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan, J, Ramalho T, Grabska-Barwinska A, et\u00a0al. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences,\u00a02017. p. 201611835.","DOI":"10.1073\/pnas.1611835114"},{"issue":"7553","key":"9784_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.","journal-title":"Nature."},{"key":"9784_CR10","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neunet.2019.04.005","volume":"116","author":"JH Lee","year":"2019","unstructured":"Lee JH. Dynmat, a network that can learn after learning. Neural Netw. 2019;116:88\u2013100.","journal-title":"Neural Netw."},{"key":"9784_CR11","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.neunet.2018.07.006","volume":"108","author":"T Lesort","year":"2018","unstructured":"Lesort T, D\u00edaz-Rodr\u00edguez N, Goudou JF, Filliat D. State representation learning for control: An overview. Neural Netw. 2018;108:379\u201392.","journal-title":"Neural Netw"},{"key":"9784_CR12","first-page":"222","volume":"1","author":"W Li","year":"2004","unstructured":"Li W, Todorov E. Iterative linear quadratic regulator design for nonlinear biological movement systems. ICINCO. 2004;1:222\u20139.","journal-title":"ICINCO"},{"issue":"12","key":"9784_CR13","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li Z, Hoiem D. Learning without forgetting. IEEE Trans Pattern Anal Mach Intell. 2017;40(12):2935\u201347.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"9784_CR14","doi-asserted-by":"crossref","unstructured":"McCloskey M, Cohen NJ. Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of learning and motivation. 1989;24:109\u2013165. Elsevier.","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"9784_CR15","unstructured":"Michalski RS, Carbonell JG, Mitchell TM. Machine learning: An artificial intelligence approach. Springer Science & Business Media. 2013."},{"key":"9784_CR16","unstructured":"Montgomery WH, Levine S. Guided policy search via approximate mirror descent. In: Advances in Neural Information Processing Systems. 2016;4008\u20134016."},{"key":"9784_CR17","doi-asserted-by":"crossref","unstructured":"Pandarinath C, OShea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann, EM, Kaufman MT, Ryu SI, Hochberg LR, et\u00a0al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nature methods. 2018. p.\u00a01","DOI":"10.1101\/152884"},{"key":"9784_CR18","doi-asserted-by":"crossref","unstructured":"Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. Continual lifelong learning with neural networks: A review. Neural Netw. 2019.","DOI":"10.1016\/j.neunet.2019.01.012"},{"issue":"7515","key":"9784_CR19","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1038\/nature13665","volume":"512","author":"PT Sadtler","year":"2014","unstructured":"Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI, Tyler-Kabara EC, Byron MY, Batista AP. Neural constraints on learning. Nature. 2014;512(7515):423.","journal-title":"Nature"},{"key":"9784_CR20","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1146\/annurev-neuro-062111-150509","volume":"36","author":"KV Shenoy","year":"2013","unstructured":"Shenoy KV, Sahani M, Churchland MM. Cortical control of arm movements: a dynamical systems perspective. Annu Rev Neurosci. 2013;36:337\u201359.","journal-title":"Annu Rev Neurosci"},{"key":"9784_CR21","unstructured":"Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In: International conference on machine learning. 2013. p. 1139\u20131147."},{"key":"9784_CR22","first-page":"181","volume":"8","author":"S Thrun","year":"1998","unstructured":"Thrun S. Lifelong learning algorithms. Learning to learn. 1998;8:181\u2013209.","journal-title":"Lifelong learning algorithms. Learning to learn"},{"key":"9784_CR23","doi-asserted-by":"crossref","unstructured":"Todorov E, Erez T, Tassa Y. Mujoco: A physics engine for model-based control. In: Intelligent Robots and Systems (IROS), 2012 IEEE\/RSJ International Conference on,\u00a0IEEE. 2012. p. 5026\u20135033.","DOI":"10.1109\/IROS.2012.6386109"},{"issue":"6","key":"9784_CR24","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1073\/pnas.0705985105","volume":"105","author":"M Umilt\u00e0","year":"2008","unstructured":"Umilt\u00e0 M, Intskirveli I, Grammont F, Rochat M, Caruana F, Jezzini A, Gallese V, Rizzolatti G, et al. When pliers become fingers in the monkey motor system. Proc Natl Acad Sc. 2008;105(6):2209\u201313.","journal-title":"Proc Natl Acad Sc"},{"key":"9784_CR25","doi-asserted-by":"crossref","unstructured":"Xiong F, Sun B, Yang X, Qiao H, Huang K, Hussain A, Liu Z. Guided policy search for sequential multitask learning. IEEE Trans Syst Man Cybern Syst. 2018;49(1):216\u201326.","DOI":"10.1109\/TSMC.2018.2800040"},{"key":"9784_CR26","first-page":"1","volume":"99","author":"X Yang","year":"2018","unstructured":"Yang X, Huang K, Zhang R, Hussain A. Learning latent features with infinite nonnegative binary matrix trifactorization. IEEETrans Emerg Top Comput Intell. 2018;99:1\u201314.","journal-title":"IEEETrans Emerg Top Comput Intell"},{"key":"9784_CR27","unstructured":"Zeng G, Chen Y, Cui B, Yu S. Continuous learning of context-dependent processing in neural networks. arXiv preprint arXiv:1810.01256 2018."},{"key":"9784_CR28","doi-asserted-by":"crossref","unstructured":"Zeng G, Chen Y, Cui B, Yu S. Continual learning of context-dependent processing in neural networks. Nature Machine Intelligence. 2019.","DOI":"10.1038\/s42256-019-0080-x"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09784-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-020-09784-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09784-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T13:27:10Z","timestamp":1615296430000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-020-09784-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["9784"],"URL":"https:\/\/doi.org\/10.1007\/s12559-020-09784-8","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2020,11,9]]},"assertion":[{"value":"9 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}