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Online Active Continual Learning for Robotic Lifelong Object Recognition

文献类型：期刊论文

作者Nie, Xiangli1,2; Deng, Zhiguang5; He, Mingdong5; Fan, Mingyu4; Tang, Zheng3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2023-09-13
页码15
ISSN号2162-237X
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)
DOI10.1109/TNNLS.2023.3308900
通讯作者Nie, Xiangli(xiangli.nie@ia.ac.cn) ; Tang, Zheng(adamtangzheng@163.com)
英文摘要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.
WOS关键词PERCEPTRON
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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001068974800001
资助机构National Natural Science Foundation of China (NNSFC) ; Beijing Nova Program ; China Electronics Technology Group Corporation (CETC) Key Laboratory of DataLink Technology
源URL[http://ir.ia.ac.cn/handle/173211/53158]  
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
3.China Elect Technol Grp Corp, Res Inst 20, Key Lab Data Link Technol, Xian 710068, Peoples R China
4.Donghua Univ, Inst Artificial Intelligence, Shanghai 200051, Peoples R China
5.Beihang Univ, Sch Software, Beijing 100191, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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