Use of Support Vector Machines in Biped Humanoid Robot for Stable Walking

안정적인 보행을 위한 이족 휴머노이드 로봇에서의 서포트 벡터 머신 이용

  • 김동원 (고려대학교 전기전자전파공학부) ;
  • 박귀태 (고려대학교 전기전자전파공학부)
  • Published : 2006.04.01


Support vector machines in biped humanoid robot are presented in this paper. The trajectory of the ZMP in biped walking robot poses an important criterion for the balance of the walking robots but complex dynamics involved make robot control difficult. We are establishing empirical relationships based on the dynamic stability of motion using SVMs. SVMs and kernel method have become very popular method for learning from examples. We applied SVM to model the practical humanoid robot. Three kinds of kernels are employed also and each result has been compared. As a result, SVM based on kernel method have been found to work well. Especially SVM with RBF kernel function provides the best results. The simulation results show that the generated ZMP from the SVM can be improve the stability of the biped walking robot and it can be effectively used to model and control practical biped walking robot.


  1. E. Neo, K. Yokoi, S. Kajita, F. Kanehiro, and K. Tanie, 'A switching command-based whole-body operation method for humanoid robots,' IEEE/SAME Trans. Mechatronics. vol. 10, no. 5, pp. 546-559, 2005
  2. M. Hirose, Y. Haikawa, and T. Takenaka, 'Introduction of hona humanoid robots development,' Proc. Adv. Sci. Ins., no. 16, pp. 1-8, 2001
  3. F. Yamasaki, T. Matsui, T. Miyashita, and H. Kitano, 'PINO-the humanoid that walks,' Proc. IEEE-RAS Int. Conf. Humanoid Robots, 2000
  4. M. Gienger, K. Leffler; and F. Pfeiffer, 'Towards the design of a biped jogging robot,' Proc. IEEE Int. Conf. Robotics Automation, pp. 4140-4145, 2001
  5. Q. Huang and Y. Nakamura, 'Sensory reflex control for humanoid walking,' IEEE Trans. Robotics, vol. 21, no. 5, pp. 977-984, 2005
  6. Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, and K. Fujiwara, 'The intelligent ASIMO: system overview and integration,' Proc. IEEE/RSJ Int. Conf. Intelli. Robots Systs., pp. 2478-2483, 2002
  7. T. Ishida, Y. Kuroki, K. Nagasaka, and J. Yamagushi, 'A small biped entertainment robot and its attractive applications,' Proc. Third IARP Int. Workshop Humanoid human Friendly Robotics, pp. 116-119, 2002
  8. C. G Atkeson, J. G Hale, F. Polick, M. Riley, S. Kotosaka, S. Schaal, T. Shibaa, G Tevatia, A. Ude, S. Vijayakumar, and E. Kawato, 'Using humanoid robots to study human behavior,' IEEE Intell. Syst., vol. 15, no. 4, pp. 46-56, 2000
  9. P. Sardain and G Bessonnet, 'Forces acting on a biped robot. center of pressure-zero moment point,' IEEE Trans Syst. Man Cybern.-A, vol. 34, no. 5, pp. 630-637, 2004
  10. M. Vukobratovic, D. Juricic, 'Contribution to the systhesis of biped gait,' IEEE Trans. Biomed, Eng., vol. 16, no. 1. pp. 1-6, 1969
  11. M. Vukobratovic, B. Brovac, 'Zero-moment point-thirty five years of its life,' Int. J. Humanoid Robotics, vol. 1, pp. 157-173, 2004
  12. D. Kim, N. H. Kim, S. J. Seo, and G. T. Park, 'Fuzzy modeling of zero moment point trajectory for a biped walking robot,' Lect. Notes Artif. Int., vol. 3214, pp. 716-722, 2005. (Best Parer Awarded Paper)
  13. D. Kim, S. J. Seo, and G. T. Park, 'Zero-moment point trajectory modeling of a biped walking robot using an adaptive neurofuzzy systems,' IEE Proc.-Control Theory Appl., vol. 152, pp. 411-426, 2005
  14. V. Vapnik, 'The Nature of Statistical Learning Theory,' John Wiley, New York, 1995
  15. S. Gunn, 'Support vector machines for classification and regression,' ISIS technical report, Image Speech & Intelligent Systems Group University of Southampton, 1998
  16. W. Wang, Z. Xu, 'A heuristic training for support vector regression,' Neurocomputing, vol. 61, pp. 259-275, 2004