Whole-Body Robot Motion Learning by Kinesthetic Teaching

  • Hsien-I Lin
  • Yung-Yao Chen
  • Yu-Che Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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.


Whole-body motion Zero-moment-point (ZMP) Kinesthetic teaching Trade-off function Genetic algorithm (GA) 


  1. 1.
    Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. and Auton. Syst. 57 (2009) 469–483CrossRefGoogle Scholar
  2. 2.
    Lin, H.I., Lai, C.C.: Learning collision-free reaching skill from primitives. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (2012) 2383–2388Google Scholar
  3. 3.
    Zollner, R., Asfour, T., Dillmann, R.: Programming by demonstration: dual-arm manipulation tasks for humanoid robots. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. Volume 1. (2004) 479–484Google Scholar
  4. 4.
    M\(\ddot{u}\)lling, K., Kober, J., Kroemer, O., Peters, J.: Learning to select and generalize striking movements in robot table tennis. Int. J. Robot. Res. 32(3) (2013) 263–279Google Scholar
  5. 5.
    Nehaniv, C.L., Dautenhahn, K.: The correspondence problem. Imitation in Animals and Artifacts (K. Dautenhahn and C. L. Nehaniv, eds., MIT Press) (2002) 41–61Google Scholar
  6. 6.
    Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T.: The development of honda humanoid robot. In: Proc. IEEE Int. Conf. Robot. Autom. Volume 2. (1998) 1321–1326Google Scholar
  7. 7.
    Erbatur, K., Okazaki, A., Obiya, K., Takenaka, T., Kawamura, A.: A study on the zero moment point measurement for biped walking robots. In: The 7th Int. Workshop Adv. Motion Control. Volume 2. (2002) 431–436Google Scholar
  8. 8.
    Nishiwaki, K., Kagami, S., Kuniyoshi, Y., Inaba, M., Inoue, H.: Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired ZMP. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. Volume 3. (2002) 2684–2689Google Scholar
  9. 9.
    Sardain, P., Bessonnet, G.: Forces acting on a biped robot. center of pressure-zero moment point. IEEE Trans. Syst., Man, Cybern. 34(5) (2004) 630–637CrossRefGoogle Scholar
  10. 10.
    Nakazawa, A., Nakaoka, S., Ikeuchi, K., Yokoi, K.: Imitating human dance motions through motion structure analysis. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. Volume 3. (2002) 2539–2544Google Scholar
  11. 11.
    Nakaoka, S., Nakazawa, A., Kanehiro, F., Kaneko, K., Morisawa, M., Ikeuchi, K.: Task model of lower body motion for a biped humanoid robot to imitate human dances. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (2005) 3157–3162Google Scholar
  12. 12.
    Miura, K., Morisawa, M., Nakaoka, S., Kanehiro, F., Harada, K., Kaneko, K., Kajita, S.: Robot motion remix based on motion capture data towards human-like locomotion of humanoid robots. In: Proc. IEEE/RSJ Int. Conf. Humanoid Robot. (2009) 596–603Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate Institute of Automation TechnologyNational Taipei University of TechnologyTaipeiTaiwan

Personalised recommendations