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Haptic recognition using hierarchical extreme learning machine with local-receptive-field

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Abstract

In order to perform useful tasks in people’s daily life, robots must be able to both communicate and understand the sensations they experience and may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose an effective hierarchical extreme learning machine with local-receptive-field architecture, while introducing the local receptive field concept in neuroscience and maintaining ELM’s advantages of training efficiency. In this paper, we further extend the LRF-based ELM method to a hierarchical model for haptic classification. Experimental validation on the Penn Haptic Adjective Corpus 2 dataset illustrates that the proposed hierarchical method achieves better recognition performance.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U1613212, 61673238, 91420302, and 61327809, in part by the National High-Tech Research and Development Plan under Grant 2015AA042306.

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Authors and Affiliations

  1. Department of Electronic Information, Taiyuan University of Technology, Shanxi, People’s Republic of China

    Fengxue Li & Xinying Xu

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, People’s Republic of China

    Huaping Liu & Fuchun Sun

  3. State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, People’s Republic of China

    Huaping Liu & Fuchun Sun

Authors
  1. Fengxue Li
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  2. Huaping Liu
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  3. Xinying Xu
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  4. Fuchun Sun
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Corresponding author

Correspondence to Huaping Liu.

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Li, F., Liu, H., Xu, X. et al. Haptic recognition using hierarchical extreme learning machine with local-receptive-field. Int. J. Mach. Learn. & Cyber. 10, 541–547 (2019). https://doi.org/10.1007/s13042-017-0736-y

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  • DOI: https://doi.org/10.1007/s13042-017-0736-y

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