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Throwing Skill Optimization through Synchronization and Desynchronization of Degree of Freedom

  • Yuji Kawai
  • Jihoon Park
  • Takato Horii
  • Yuji Oshima
  • Kazuaki Tanaka
  • Hiroki Mori
  • Yukie Nagai
  • Takashi Takuma
  • Minoru Asada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7500)

Abstract

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.

Keywords

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.

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References

  1. 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. 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. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 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. 8.
    Bernstein, N.A.: The co-ordination and regulation of movements. Pergamon Press (1967)Google Scholar
  9. 9.
    Newell, K.M., Vaillancourt, D.E.: Dimensional change in motor learning. Human Movement Science 20(4-5), 695–715 (2001)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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. 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. 14.
    Reilly, T.: Science and Soccer, Routledge (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuji Kawai
    • 1
  • Jihoon Park
    • 1
  • Takato Horii
    • 1
  • Yuji Oshima
    • 1
  • Kazuaki Tanaka
    • 1
    • 2
  • Hiroki Mori
    • 1
  • Yukie Nagai
    • 1
  • Takashi Takuma
    • 3
  • Minoru Asada
    • 1
  1. 1.Dept. of Adaptive Machine Systems, Graduate School of EngineeringOsaka UniversityOsakaJapan
  2. 2.CRESTJapan Science and Technology AgencyJapan
  3. 3.Dept. of Electrical and Electronic Systems EngineeringOsaka Institute of TechnologyOsakaJapan

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