State Primitive Learning to Overcome Catastrophic Forgetting in Robotics

doi:10.1007/s12559-020-09784-8

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	State Primitive Learning to Overcome Catastrophic Forgetting in Robotics
	Xiong, Fangzhou1,2 ; Liu, Zhiyong1,2,3 ; Huang, Kaizhu 4; Yang, Xu1 ; Qiao, Hong1,2,3
发表期刊	COGNITIVE COMPUTATION
ISSN	1866-9956
	2020-11-09
页码	9
通讯作者	Liu, Zhiyong(zhiyong.liu@ia.ac.cn)
摘要	People 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.
关键词	Catastrophic forgetting State primitives Robotics Continual learning
DOI	10.1007/s12559-020-09784-8
收录类别	SCI
语种	英语
资助项目	National 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]
项目资助者	National 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研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000587981300002
出版者	SPRINGER
七大方向——子方向分类	强化与进化学习
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/41747
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Liu, Zhiyong
作者单位	1.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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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.