Skip to main content

Throwing Skill Optimization through Synchronization and Desynchronization of Degree of Freedom

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7500)


Humanoid robots have a large number of degrees of freedom (DoFs), therefore motor learning by such robots which explore the optimal parameters of behaviors is one of the most serious issues in humanoid robotics. In contrast, it has been suggested that humans can solve such a problem by synchronizing many body parts in the early stage of learning, and then desynchronizing their movements to optimize a behavior for a task. This is called as ”Freeze and Release.” We hypothesize that heuristic exploration through synchronization and desynchronization of DoFs accelerates motor learning of humanoid robots. In this paper, we applied this heuristic to a throwing skill learning in soccer. First, all motors related to the skill are actuated in a synchronized manner, thus the robot explores optimal timing of releasing a ball in one-dimensional search space. The DoFs are released gradually, which allows to search for the best timing to actuate the motors of all joints. The real robot experiments showed that the exploration method was fast and practical because the solution in low-dimensional subspace was approximately optimum.


  • Particle Swarm Optimization
  • Search Space
  • Motor Learning
  • Humanoid Robot
  • Hill Climbing

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Hornby, G.S., Fujita, M., Takamura, S., Yamamoto, T., Hanagata, O.: Autonomous evolution of gaits with the sony quadruped robot. In: Proc. of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1297–1304 (1999)

    Google Scholar 

  2. Daoxiong, G., Jie, Y., Guoyu, Z.: A review of gait optimization based on evolutionary computation. Applied Computational Intelligence and Soft Computing (2010)

    Google Scholar 

  3. Rong, C., Wang, Q., Huang, Y., Xie, G., Wang, L.: Autonomous evolution of high-speed quadruped gaits using particle swarm optimization. In: Iocchi, L., Matsubara, H., Weitzenfeld, A., Zhou, C. (eds.) RoboCup 2008. LNCS, vol. 5399, pp. 259–270. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  4. Shafii, N., Aslani, S., Nezami, O.M., Shiry, S.: Evolution of biped walking using truncated fourier series and particle swarm optimization. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 344–354. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  5. Kohl, N., Stone, P.: Machine learning for fast quadrupedal locomotion. In: Proc. of the 19th National Conf. on Artificial Intelligence, pp. 611–616 (2004)

    Google Scholar 

  6. Saggar, M., D’Silva, T., Kohl, N., Stone, P.: Autonomous learning of stable quadruped locomotion. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 98–109. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  7. Hausknecht, M., Stone, P.: Learning powerful kicks on the aibo ERS-7: The quest for a striker. In: Ruiz-del-Solar, J. (ed.) RoboCup 2010. LNCS, vol. 6556, pp. 254–265. Springer, Heidelberg (2010)

    Google Scholar 

  8. Bernstein, N.A.: The co-ordination and regulation of movements. Pergamon Press (1967)

    Google Scholar 

  9. Newell, K.M., Vaillancourt, D.E.: Dimensional change in motor learning. Human Movement Science 20(4-5), 695–715 (2001)

    CrossRef  Google Scholar 

  10. Vereijken, B., van Emmerik, R.E.A., Whiting, H.T.A., Newell, K.M.: Free(z)ing degrees of freedom in skill acquisition. Journal of Motor Behavior 24(1), 133–142 (1992)

    CrossRef  Google Scholar 

  11. Yamamoto, T., Fujinami, T.: Hierarchical organization of the coordinative structure of the skill of clay kneading. Human Movement Science 27(5), 812–822 (2008)

    CrossRef  Google Scholar 

  12. Matsumura, K., Yamamoto, T., Fujinami, T.: A study of samba dance using acceleration sensors. In: Proc. of the 8th Motor Control and Human Skill Conference, pp. 5–4 (2007)

    Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  14. Reilly, T.: Science and Soccer, Routledge (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kawai, Y. et al. (2013). Throwing Skill Optimization through Synchronization and Desynchronization of Degree of Freedom. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds) RoboCup 2012: Robot Soccer World Cup XVI. RoboCup 2012. Lecture Notes in Computer Science(), vol 7500. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39249-8

  • Online ISBN: 978-3-642-39250-4

  • eBook Packages: Computer ScienceComputer Science (R0)