Throwing Skill Optimization through Synchronization and Desynchronization of Degree of Freedom
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.
Unable to display preview. Download preview PDF.
- 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
- 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.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
- 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
- 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