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Robotic Grasp Stability Analysis Using Extreme Learning Machine

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Proceedings of ELM-2016

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 9))

Abstract

Recently, autonomous grasping of unknown objects is a fundamental requirement for robots performing manipulation tasks in real world environments. It is still considered as a challenging problem no matter how process we have made. It is significant that how the robot to judge the stability of grabbing object. In this paper, we analyze the data through process of grabbing 3 objects whether is successful or failed by constructing Global Alignment kernel with Extreme Learning Machine and Support Vector Machine. For comparative analysis, the Barrett hand’s finger angles and robot joint angles are also recorded. By processing obtained data in different ways, we have comparative results in various modes. Experiments denote the tactile results achieve better performance than the finger angle’s and robot joint angle’s.

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Acknowledgements

This work was supported in part by the National Key Project for Basic Research, China, under Grant 2013CB329403, in part by the National Natural Science Foundation of China under Grants 61673238, 91420302 and 61327809, and in part by the National High-Tech Research and Development Plan under Grant 2015AA042306.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China

    Peng Bai & Meng Gao

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

    Huaping Liu & Fuchun Sun

  3. State Key Laboratory of Intelligent Technology and Systems, Tsinghua University TNLIST, Beijing, China

    Peng Bai, Huaping Liu & Fuchun Sun

Authors
  1. Peng Bai
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  2. Huaping Liu
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  3. Fuchun Sun
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  4. Meng Gao
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Corresponding author

Correspondence to Huaping Liu .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  3. Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

  4. Department of Information and Computer Science, School of Science, Aalto University, Aalto, Finland

    Yoan Miche

  5. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

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Bai, P., Liu, H., Sun, F., Gao, M. (2018). Robotic Grasp Stability Analysis Using Extreme Learning Machine. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds) Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-57421-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-57421-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57420-2

  • Online ISBN: 978-3-319-57421-9

  • eBook Packages: EngineeringEngineering (R0)

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