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Tactile Object Recognition Using Supervised Dictionary Learning

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Robotic Tactile Perception and Understanding

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Abstract

This chapter addresses the tactile object recognition problem by developing an extreme kernel sparse learning methodology. This method combines the advantages of extreme learning machine (ELM) and kernel sparse learning by simultaneously addressing the dictionary learning and the classifier design problems. Furthermore, to tackle the intrinsic difficulties which are introduced by the Representer Theorem, a reduced kernel dictionary learning method is developed by introducing row-sparsity constraint. A globally convergent algorithm is developed to solve the optimization problem, and the theoretical proof is provided. Finally, extensive experimental validations on some public available tactile sequence datasets show the advantages of the proposed method.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu & Fuchun Sun

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  1. Huaping Liu
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  2. Fuchun Sun
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Correspondence to Huaping Liu .

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Liu, H., Sun, F. (2018). Tactile Object Recognition Using Supervised Dictionary Learning. In: Robotic Tactile Perception and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-10-6171-4_4

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  • DOI: https://doi.org/10.1007/978-981-10-6171-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6170-7

  • Online ISBN: 978-981-10-6171-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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