State Primitive Learning to Overcome Catastrophic Forgetting in Robotics
Xiong, Fangzhou1,2; Liu, Zhiyong1,2,3; Huang, Kaizhu4; Yang, Xu1; Qiao, Hong1,2,3
Source PublicationCOGNITIVE COMPUTATION
ISSN1866-9956
2020-11-09
Pages9
Corresponding AuthorLiu, Zhiyong(zhiyong.liu@ia.ac.cn)
AbstractPeople can learn continuously a wide range of tasks without catastrophic forgetting. To mimic this functioning of continual learning, current methods mainly focus on studying a one-step supervised learning problem, e.g., image classification. They aim to retain the performance of previous image classification results when neural networks are sequentially trained on new images. In this paper, we concentrate on solving multi-step robotic tasks sequentially with the proposed architecture called state primitive learning. By projecting the original state space into a low-dimensional representation, meaningful state primitives can be generated to describe tasks. Under two kinds of different constraints on the generation of state primitives, control signals corresponding to different robotic tasks can be separately addressed only with an efficient linear regression. Experiments on several robotic manipulation tasks demonstrate the new method efficacy to learn control signals under the scenario of continual learning, delivering substantially improved performance over the other comparison methods.
KeywordCatastrophic forgetting State primitives Robotics Continual learning
DOI10.1007/s12559-020-09784-8
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China[2017YFB1300202] ; NSFC[U1613213] ; NSFC[61375005] ; NSFC[61503383] ; NSFC[61210009] ; NSFC[61876155] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; Dongguan core technology research frontier project[2019622101001] ; Natural Science Foundation of Jiangsu Province[BK20181189] ; Strategic Priority Research Program of the CAS[XDB02080003] ; Key Program Special Fund in XJTLU[KSF-A-01] ; Key Program Special Fund in XJTLU[KSF-E-26] ; Key Program Special Fund in XJTLU[KSF-P-02]
Funding OrganizationNational Key Research and Development Plan of China ; NSFC ; Strategic Priority Research Program of Chinese Academy of Science ; Dongguan core technology research frontier project ; Natural Science Foundation of Jiangsu Province ; Strategic Priority Research Program of the CAS ; Key Program Special Fund in XJTLU
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000587981300002
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41747
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorLiu, Zhiyong
Affiliation1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
4.Xian Jiaotong Liverpool Univ, Dept EEE, Renai Rd 111, Suzhou 215123, Jiangsu, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiong, Fangzhou,Liu, Zhiyong,Huang, Kaizhu,et al. State Primitive Learning to Overcome Catastrophic Forgetting in Robotics[J]. COGNITIVE COMPUTATION,2020:9.
APA Xiong, Fangzhou,Liu, Zhiyong,Huang, Kaizhu,Yang, Xu,&Qiao, Hong.(2020).State Primitive Learning to Overcome Catastrophic Forgetting in Robotics.COGNITIVE COMPUTATION,9.
MLA Xiong, Fangzhou,et al."State Primitive Learning to Overcome Catastrophic Forgetting in Robotics".COGNITIVE COMPUTATION (2020):9.
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