Kick Motions for the NAO Robot Using Dynamic Movement Primitives

  • Arne BöckmannEmail author
  • Tim Laue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)


In this paper, we present the probably first application of the popular Dynamic Movement Primitives (DMP) approach to the domain of soccer-playing humanoid robots. DMPs are known for their ability to imitate previously demonstrated motions as well as to flexibly adapt to unforeseen changes to the desired trajectory with respect to speed and direction. As demonstrated in this paper, this makes them a useful approach for describing kick motions. Furthermore, we present a mathematical motor model that compensates for the NAO robot’s motor control delay as well as a novel minor extension to the DMP formulation. The motor model is used in the calculation of the Zero Moment Point (ZMP), which is needed to keep the robot in balance while kicking. All approaches have been evaluated on real NAO robots.



We would like to thank the members of the team B-Human for providing the software framework for this work.


  1. 1.
    Alcaraz-Jiménez, J.J., Herrero-Pérez, D., Martínez-Barberá, H.: Robust feedback control of ZMP-based gait for the humanoid robot Nao. Int. J. Robot. Res. 32(9–10), 1074–1088 (2013)CrossRefGoogle Scholar
  2. 2.
    Czarnetzki, S., Kerner, S., Urbann, O.: Applying dynamic walking control for biped robots. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 69–80. Springer, Heidelberg (2010). Scholar
  3. 3.
    Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Serre, J., Maisonnier, B.: Mechatronic design of NAO humanoid. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA 2009), Kobe, Japan, pp. 2124–2129 (2009)Google Scholar
  4. 4.
    Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K., Hirukawa, H.: Biped walking pattern generation by using preview control of zero-moment point. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA 2003), Taipei, Taiwan, vol. 2, pp. 1620–1626 (2003)Google Scholar
  6. 6.
    Kober, J., Mülling, K., Krömer, O., Lampert, C.H., Scholkopf, B., Peters, J.: Movement templates for learning of hitting and batting. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, USA, pp. 853–858 (2010)Google Scholar
  7. 7.
    Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013)CrossRefGoogle Scholar
  8. 8.
    Müller, J., Laue, T., Röfer, T.: Kicking a ball – modeling complex dynamic motions for humanoid robots. In: Ruiz-del-Solar, J., Chown, E., Plöger, P.G. (eds.) RoboCup 2010. LNCS, vol. 6556, pp. 109–120. Springer, Heidelberg (2011). Scholar
  9. 9.
    Mülling, K., Kober, J., Krömer, O., Peters, J.: Learning to select and generalize striking movements in robot table tennis. Int. J. Robot. Res. 32(3), 263–279 (2013)CrossRefGoogle Scholar
  10. 10.
    Royston, J.: Some techniques for assessing multivarate normality based on the Shapiro-Wilk W. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 32(2), 121–133 (1983)zbMATHGoogle Scholar
  11. 11.
    Röfer, T., Laue, T., Richter-Klug, J., Schünemann, M., Stiensmeier, J., Stolpmann, A., Stöwing, A., Thielke, F.: B-Human Team Report and Code Release 2015 (2015).
  12. 12.
    Sardain, P., Bessonnet, G.: Forces acting on a biped robot. Center of pressure - zero moment point. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 34(5), 630–637 (2004)CrossRefGoogle Scholar
  13. 13.
    Schaal, S.: Dynamic movement primitives - a framework for motor control in humans and humanoid robotics. In: Kimura, H., Tsuchiya, K., Ishiguro, A., Witte, H. (eds.) Adaptive Motion of Animals and Machines, pp. 261–280. Springer, Tokyo (2006). Scholar
  14. 14.
    Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.: Control, planning, learning, and imitation with dynamic movement primitives. In: Proceedings of the Workshop on Bilateral Paradigms on Humans and Humanoids, IEEE International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, Nevada, USA, pp. 1–21 (2003)Google Scholar
  15. 15.
    Ude, A., Nemec, B., Petric, T., Morimoto, J.: Orientation in cartesian space dynamic movement primitives. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong, China, pp. 2997–3004 (2014)Google Scholar
  16. 16.
    Urbann, O., Tasse, S.: Observer based biped walking control, a sensor fusion approach. Auton. Robots 35(1), 37–49 (2013)CrossRefGoogle Scholar
  17. 17.
    Vukobratović, M., Borovac, B.: Zero-moment point—thirty five years of its life. Int. J. Humanoid Rob. 1(1), 157–173 (2004)CrossRefGoogle Scholar
  18. 18.
    Wenk, F., Röfer, T.: Online generated kick motions for the NAO balanced using inverse dynamics. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS, vol. 8371, pp. 25–36. Springer, Heidelberg (2014). Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Fachbereich 3 – Mathematik und InformatikUniversität BremenBremenGermany

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