Online Active Continual Learning for Robotic Lifelong Object Recognition

doi:10.1109/TNNLS.2023.3308900

	Online Active Continual Learning for Robotic Lifelong Object Recognition
	Nie, Xiangli1,2 ; Deng, Zhiguang 3; He, Mingdong 3; Fan, Mingyu 4; Tang, Zheng 5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2023-09-13
页码	15
通讯作者	Nie, Xiangli(xiangli.nie@ia.ac.cn) ; Tang, Zheng(adamtangzheng@163.com)
摘要	In real-world applications, robotic systems collect vast amounts of new data from ever-changing environments over time. They need to continually interact and learn new knowledge from the external world to adapt to the environment. Particularly, lifelong object recognition in an online and interactive manner is a crucial and fundamental capability for robotic systems. To meet this realistic demand, in this article, we propose an online active continual learning (OACL) framework for robotic lifelong object recognition, in the scenario of both classes and domains changing with dynamic environments. First, to reduce the labeling cost as much as possible while maximizing the performance, a new online active learning (OAL) strategy is designed by taking both the uncertainty and diversity of samples into account to protect the information volume and distribution of data. In addition, to prevent catastrophic forgetting and reduce memory costs, a novel online continual learning (OCL) algorithm is proposed based on the deep feature semantic augmentation and a new loss-based deep model and replay buffer update, which can mitigate the class imbalance between the old and new classes and alleviate confusion between two similar classes. Moreover, the mistake bound of the proposed method is analyzed in theory. OACL allows robots to select the most representative new samples to query labels and continually learn new objects and new variants of previously learned objects from a nonindependent and identically distributed (i.i.d.) data stream without catastrophic forgetting. Extensive experiments conducted on real lifelong robotic vision datasets demonstrate that our algorithm, even trained with fewer labeled samples and replay exemplars, can achieve state-of-the-art performance on OCL tasks.
关键词	Task analysis Robots Data models Object recognition Learning systems Training Heuristic algorithms Catastrophic forgetting lifelong object recognition online active learning (OAL) online continual learning (OCL)
DOI	10.1109/TNNLS.2023.3308900
关键词[WOS]	PERCEPTRON
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NNSFC)[62076241] ; National Natural Science Foundation of China (NNSFC)[91948303] ; Beijing Nova Program[61933001] ; China Electronics Technology Group Corporation (CETC) Key Laboratory of DataLink Technology[20220484070] ; [CLDL-20202208_1]
项目资助者	National Natural Science Foundation of China (NNSFC) ; Beijing Nova Program ; China Electronics Technology Group Corporation (CETC) Key Laboratory of DataLink Technology
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001068974800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[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/173211/53159
专题	多模态人工智能系统全国重点实验室
通讯作者	Nie, Xiangli; Tang, Zheng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beihang Univ, Sch Software, Beijing 100191, Peoples R China 4.Donghua Univ, Inst Artificial Intelligence, Shanghai 200051, Peoples R China 5.China Elect Technol Grp Corp, Res Inst 20, Key Lab Data Link Technol, Xian 710068, 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	Nie, Xiangli,Deng, Zhiguang,He, Mingdong,et al. Online Active Continual Learning for Robotic Lifelong Object Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:15.
APA	Nie, Xiangli,Deng, Zhiguang,He, Mingdong,Fan, Mingyu,&Tang, Zheng.(2023).Online Active Continual Learning for Robotic Lifelong Object Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Nie, Xiangli,et al."Online Active Continual Learning for Robotic Lifelong Object Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):15.