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Intelligent Autonomous Systems 13 pp 1467–1479Cite as

Whole-Body Robot Motion Learning by Kinesthetic Teaching

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

Learning whole-body robot motion is a challenging task because balance control should be taken into consideration. An intuitive way to teach motion to a humanoid robot is to apply human demonstration data to the robot. Since balance control was usually done by presetting the zero-moment-point (ZMP) trajectory of a robot, the challenge became the conversion problem from human motion to robot motion, making the ZMP trajectory satisfy the stability. In this paper, we use kinesthetic teaching to teach whole-body robot motion by directly pulling the limbs of a robot without any conversion from human to robot motion. To keep the robot balanced, we propose a trade-off function by considering motion similarity and balance simultaneously and adopt the genetic algorithm (GA) to find the solution for adapting the taught motion. We validated the proposed method on an Aldebaran NAO robot and the results showed that the robot was taught to perform side and back kicks via kinesthetic teaching.

This work was supported in part by the National Science Council under Grant NSC 102-2221-E-027-085 and 102-2218-E-027-016-MY2. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Council.

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Authors and Affiliations

  1. Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan

    Hsien-I Lin, Yung-Yao Chen & Yu-Che Huang

Authors
  1. Hsien-I Lin
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  2. Yung-Yao Chen
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  3. Yu-Che Huang
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Corresponding author

Correspondence to Hsien-I Lin .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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© 2016 Springer International Publishing Switzerland

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Lin, HI., Chen, YY., Huang, YC. (2016). Whole-Body Robot Motion Learning by Kinesthetic Teaching. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_105

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_105

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

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