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Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

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Abstract

Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine (OM-ELM) which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning (MKL) problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 613278050 and 61210013). The work of Hongbo Li was supported in part by the National Natural Science Foundation of China (Grant No. 61473161), and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAK12B03).

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Lele Cao, Fuchun Sun, Hongbo Li & Wenbing Huang

  2. Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Lele Cao, Fuchun Sun, Hongbo Li & Wenbing Huang

  3. Department of Computing and Information Systems, The University of Melbourne, Parkville, 3010, VIC, Australia

    Lele Cao

Authors
  1. Lele Cao
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  2. Fuchun Sun
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  3. Hongbo Li
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  4. Wenbing Huang
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Correspondence to Lele Cao.

Additional information

Lele Cao received the MS in Interactive systems engineering from KTH Royal Institute of Technology, Sweden in 2010. He is currently a PhD candidate in Department of Computer Science and Technology, Tsinghua University, China. His research interests include machine learning, neural networks, and human computer interaction.

Fuchun Sun received his PhD from the Department of Computer Science and Technology, Tsinghua University, China in 1998. He is currently a professor with the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control, neural networks, fuzzy systems, variable structure control, nonlinear systems, information fusion, and robotics.

Hongbo Li received his PhD from Department of Computer Science and Technology, Tsinghua University, China in 2009. He is currently an assistant professor with the Department of Computer Science and Technology, Tsinghua University. His research interests include networked control systems and intelligent control.

Wenbing Huang received his BS in applied mathematics from Beihang University, China in 2012. He is currently a PhD candidate in Department of Computer Science and Technology, Tsinghua University, China. His research interest is machine learning.

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Cao, L., Sun, F., Li, H. et al. Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine. Front. Comput. Sci. 11, 276–289 (2017). https://doi.org/10.1007/s11704-016-5171-9

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