Reflex Control

  • Riadh ZaierEmail author
Reference work entry


Reflexes have been viewed as integrated motions with the centrally generated motors commands to produce adaptive movement. Lateral and frontal disturbances during locomotion due to terrain irregularities have been dealt with using conventional sensory feedback that was realized based on the inverse pendulum model. This chapter deals with reflexes that highly adapt and control the movement of the humanoid robot when a large disturbance occurs. The reflex action consists of modulating the motors’ commands by the outputs from both the force sensors located under the robot legs and the gyro sensor located at the robot’s upper body. A primitive neural network can deal with simple reflexes. These reflexes can be improved further to robustly address particular classes of sudden events. Eventually, in this chapter, primitive reflex against sudden obstacles is improved by the afferent signals in order to be more adaptable and robust against unexpected obstacle hitting the robot sole plate at random locations. The modified adaptive reflex consists of increasing the support polygon by controlling the ankle joint of the leg touching the obstacle. With such adaptation, the reflex response can be coordinated and modulated with locomotion controller’s outputs to achieve an intended stabilizing behavior of the robot.


  1. 1.
    H. Miura, I. Shimoyama, Dynamic walk of a biped. Int. J. Robot. Res.3(2), 60–74 (1984)CrossRefGoogle Scholar
  2. 2.
    S. Kajita, O. Matsumoto, Real-time 3D walking pattern generation for a biped robot with telescopic legs, in Proceedings of the 2001 IEEE International Conference on Robotics & Automation, 2001, pp. 2299–2306Google Scholar
  3. 3.
    Q. Huang, K. Yokoi, S. Kajita, K. Kaneko, N. Koyachi, H. Arai, K. Tanie, Planning walking patterns for a biped robot. IEEE Trans. Robot. Autom. 17(3), 280–289 (2001)Google Scholar
  4. 4.
    Q. Huang, Y. Nakamura, Sensory reflex control for humanoid walking. IEEE Trans. Robot. Autom. 21(5), 977–984 (2005)CrossRefGoogle Scholar
  5. 5.
    S. Grillner, Neurobiological bases of rhythmic motor acts in vertebrates. Science. 228, 143–149 (1985)CrossRefGoogle Scholar
  6. 6.
    A.J. Ijspeert, J. Kodjabachia, Evolution and development of a central pattern generator for the swimming of a Lamprey. Artif. Life.5(3), 247–269, 1999CrossRefGoogle Scholar
  7. 7.
    P.A. Guertin, Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. Front. Neurol. 3, 4–15 (2013)Google Scholar
  8. 8.
    A.J Ijspeert, Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21(4), 642–653 (2008)CrossRefGoogle Scholar
  9. 9.
    G. Taga, A model of the neuro-musculo-skeletal system for human locomotion, I. Emergence of basic gait. Boil. Cybern. 73, 97–111 (1995)CrossRefGoogle Scholar
  10. 10.
    G. Taga, Y. Yamaguchi, H. Shimizu, Self organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol. Cybern. 65, 147–159 (1991)CrossRefGoogle Scholar
  11. 11.
    A.J. Ijspeert, A. Crespi, D. Ryczko, J.M. Cabelguen, From swimming to walking with a salamander robot driven by a spinal cord model. Science. 315(5817), 1416–1420 (2007)CrossRefGoogle Scholar
  12. 12.
    L. Righetti, A.J. Ijspeert, Programmable central pattern generators: an application to biped locomotion control, in Proceedings of IEEE International Conference on Robotics & Automation, Orlando, 2006Google Scholar
  13. 13.
    L. Righetti, A.J. Ijspeert, Pattern generators with sensory feedback for the control of quadruped locomotion, in Proceedings of IEEE International Conference on Robotics & Automation, 2008, pp. 2188–2195Google Scholar
  14. 14.
    L. Righetti, A.J. Ijspeert, Design methodologies for central pattern generators: an application to crawling humanoids, in Proceedings of Robotics: Science and Systems, 2006Google Scholar
  15. 15.
    M. Morisawa, S. Kajita, K. Harada, K. Fujiwara, Emergency stop algorithm for walking humanoid robots, in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 2109–2115Google Scholar
  16. 16.
    M. Okada, K. Osato, Y. Nakamura, Motion emergency of humanoid robots by an attractor design of a nonlinear dynamics, in IEEE International Conference on Robotics and Automation, 2005, pp. 18–23Google Scholar
  17. 17.
    R. Zaier, F. Nagashima, Recurrent neural network language for robot learning, in The 20th Annual Conf. of the Robotics Society of Japan, Osaka, Japan Oct 2002Google Scholar
  18. 18.
    R. Zaier, F. Nagashima, Motion generation of humanoid robot based on polynomials generated by recurrent neural network, in Proceedings of the First Asia International Symposium on Mechatronics, 2004, pp. 659–664Google Scholar
  19. 19.
    W. Gerstner, Time structure of the activity in neural network models. Phys. Rev. E. 51, 738–758 (1995)CrossRefGoogle Scholar
  20. 20.
    R. Zaier, F. Nagashima, Motion pattern generator and reflex system for humanoid robots, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006Google Scholar
  21. 21.
    R. Zaier, S. Kanda, Piecewise-linear pattern generator and reflex system for humanoid robots, in Proceedings of IEEE International Conference on Robotics & Automation, pp. 2188–2195, 2007Google Scholar
  22. 22.
    R. Zaier, Experimental study of sensory reflex for humanoid robots: activation and stability issues, in 25th Annual Conference of the Robotics Society of Japan, 2007Google Scholar
  23. 23.
    C.S. Sherrington. Encyclopædia Britannica, Inc. Retrieved 31 July 2012Google Scholar
  24. 24.
    E.R. Kandel, J.H. Schwartz, T.M. Jessell, Kandel_Principles of Neural Science, Part VI, 5th edn. McGraw-Hill (2013)Google Scholar
  25. 25.
    R.A. Dicaprio, F. Clarac, Reversal of a walking leg reflex elicited by a muscles receptor. J. Exp. Biol. 90, 197–203 (1981)Google Scholar
  26. 26.
    H. Wolf, K.G. Pearson, Proprioceptive input patterns elevator activity in the locust flight system. J. Neurophysiol. 59(6), 1831–1853 (1988)CrossRefGoogle Scholar
  27. 27.
    H.K. Khalil, Nonlinear Systems (MacMillan, New York, 1996). Chap. 3Google Scholar
  28. 28.
  29. 29.
    HOAP-3, Fujitsu Automation Ltd. Available at:
  30. 30.
    R. Zaier, J. Abdo, Legged vehicle control and vibration reduction. Int. J. Veh. Noise Vib. 8(1), 74–94 (2012)CrossRefGoogle Scholar
  31. 31.
    R. Zaier, Reflex system for humanoid robots against large disturbances, in The 24th Annual Conference of the Robotics Society of Japan, Okayama, 2006)Google Scholar
  32. 32.
    J.M. Goncalves, A. Megretski, M.A. Dahleh, Global stability of relay feedback systems, in Proceedings of the American Control Conference, 2000, pp. 220–224Google Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial EngineeringCollege of Engineering, Sultan Qaboos UniversityAl KhodSultanate of Oman

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