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Fully Automated Learning for Position and Contact Force of Manipulated Object with Wired Flexible Finger Joints

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

We discuss about the modeling technology in the object manipulation of the robot arm that is equipped with flexible finger joints. In recent years, flexible robot fingers are getting attention because of their handling capability and safety. However, the position and contact force of manipulated object take much non-linear uncertainty from the flexibility. In this paper, we propose the modeling framework of the position and contact force of the manipulated object. The proposed framework is an online learning method that is composed of motor babbling, dynamics learning tree (DLT), and \(\epsilon \)-greedy method. In the experiments, the effectiveness of DLT was compared with neural network (NN), the effectiveness of the proposed framework was validated using a drawing task of a humanoid robot that equipped with flexible finger joints. The proposed framework was able to realize a fully automated incremental-manipulation-learning.

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Notes

  1. 1.

    The same algorithm is used in another proposition [17] that is presented in this conference.

  2. 2.

    The humanoid robot NAO was placed on the top of the figures. It moved a pen using its right hand. The result of Exploitation mode has a bias compared with the others. This might be because Exploitation mode searches pen tip positions conservatively by exploiting the knowledge of DLT.

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Acknowledgement

This work was supported by JSPS KAKENHI Grant Numbers, 15K00363, 15K20850, 24119003, 25730159, and SCAT Technology Research Foundation.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Department of Electrical Engineering and Computer Science, Iwate University, Iwate, Japan

    Kanta Watanabe & Chyon Hae Kim

  2. Institute of Technology and Science, Tokushima University, Tokushima, Japan

    Shun Nishide

  3. Faculty of Engineering, Department of Mechanical Engineering and Intelligent Systems, Tohoku Gakuin University, Sendai, Japan

    Manabu Gouko

Authors
  1. Kanta Watanabe
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  2. Shun Nishide
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  3. Manabu Gouko
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  4. Chyon Hae Kim
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Corresponding author

Correspondence to Chyon Hae Kim .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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Watanabe, K., Nishide, S., Gouko, M., Kim, C.H. (2016). Fully Automated Learning for Position and Contact Force of Manipulated Object with Wired Flexible Finger Joints. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_64

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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