Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots


In this paper, we present an extended mathematical model of the central pattern generator (CPG) in the spinal cord. The proposed CPG model is used as the underlying low-level controller of a humanoid robot to generate various walking patterns. Such biological mechanisms have been demonstrated to be robust in locomotion of animal. Our model is supported by two neurophysiological studies. The first study identified a neural circuitry consisting of a two-layered CPG, in which pattern formation and rhythm generation are produced at different levels. The second study focused on a specific neural model that can generate different patterns, including oscillation. This neural model was employed in the pattern generation layer of our CPG, which enables it to produce different motion patterns—rhythmic as well as non-rhythmic motions. Due to the pattern-formation layer, the CPG is able to produce behaviors related to the dominating rhythm (extension/flexion) and rhythm deletion without rhythm resetting. The proposed multi-layered multi-pattern CPG model (MLMP-CPG) has been deployed in a 3D humanoid robot (NAO) while it performs locomotion tasks. The effectiveness of our model is demonstrated in simulations and through experimental results.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. Amrollah E, Henaff P (2010) On the role of sensory feedbacks in rowat-selverston cpg to improve robot legged locomotion. Front Neurorobot 4:00113

    Article  Google Scholar 

  2. Brown GT (1911) The intrinsic factors in the act of progression in the mammal. Proc R Soc Lond 84(572):308–319

    Article  Google Scholar 

  3. Brown TG (1914) On the fundamental activity of the nervous centres: together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. J Physiol 48(1):18–46

    CAS  PubMed Central  PubMed  Google Scholar 

  4. Choi JT, Bastian AJ (2007) Adaptation reveals independent control networks for human walking. Nat Neurosci 10(8):1055–1062

    CAS  PubMed  Article  Google Scholar 

  5. Cunningham CB, Schilling N, Anders C, Carrier DR (2010) The influence of foot posture on the cost of transport in humans. J Exp Biol 5(213):790–797

    Article  Google Scholar 

  6. Degallier S, Righetti L, Gay S, Ijspeert A (2011) Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives. Auton Robots 31(2–3):155–181

    Article  Google Scholar 

  7. Endo G, Morimoto J, Matsubara T, Nakanishi J, Cheng G (2008) Learning cpg-based biped locomotion with a policy gradient method: application to a humanoid robot. Int J Robot Res 27:213–228

    Article  Google Scholar 

  8. Endo G, Morimoto J, Nakanishi J, Cheng G (2004) An empirical exploration of a neural oscillator for biped locomotion control. In: Proceedings of the 2004 IEEE international conference on robotics and automation, ICRA 2004, April 26–May 1, 2004. LA, USA, New Orleans, pp 3036–3042

  9. Geng T, Porr B, Wörgötter F (2006) Fast biped walking with a sensor-driven neuronal controller and real-time online learning. Int J Robot Res 25:243–259

    Article  Google Scholar 

  10. Geyer H, Herr H (2010) A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans Neural Syst Rehabil Eng 18(3):263–273

    PubMed  Article  Google Scholar 

  11. Haken H, Kelso JAS, Bunz H, Haken H, Kelso JAS, Bunz H (1985) A theoretical model of phase transitions in human hand movements. Biol Cybern 51(5):347–356

    CAS  PubMed  Article  Google Scholar 

  12. Hoinville T (2007) Évolution de contrôleurs neuronaux plastiques : de la locomotion adaptée vers la locomotion adaptative. Ph.D. dissertation, University of Versailles St Quentin, Vélizy, France

  13. Ijspeert AJ, Crespi A, Ryczko D, Cabelguen J-M (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817):1416–1420

    CAS  PubMed  Article  Google Scholar 

  14. Koshland GF, Smith JL (1989) Mutable and immutable features of paw-shake responses after hindlimb deafferentation in the cat. J Neurophysiol 62(1):162–173

    CAS  PubMed  Google Scholar 

  15. Lafreniere-Roula M, McCrea DA (2005) Deletions of rhythmic motoneuron activity during fictive locomotion and scratch provide clues to the organization of the mammalian central pattern generator. J Neurophysiol 94(2):1120–1132

    PubMed  Article  Google Scholar 

  16. Liu GL, Habib M, Watanabe K, Izumi K (2007) Cpg based control for generating stable bipedal trajectories under external perturbation. In: SICE, 2007 Annual conference, pp 1019–1022

  17. Liu G, Habib M, Watanabe K, Izumi K (2008) Central pattern generators based on matsuoka oscillators for the locomotion of biped robots. Artif Life Robot 12(1):264–269

    Article  Google Scholar 

  18. Manoonpong P, Geng T, Kulvicius T, Porr B, Wörgötter F (2007) Adaptive, fast walking in a biped robot under neuronal control and learning. PLoS Comput Biol 3(7):e134

    PubMed Central  PubMed  Article  Google Scholar 

  19. Marder E, Bucher D (2001) Central pattern generators and the control of rhythmic movements. Curr Biol 11(23):R986–R996

    CAS  PubMed  Article  Google Scholar 

  20. Markin SN, Klishko AN, Shevtsova NA, Lemay MA, Prilutsky BI, Rybak IA (2010) Afferent control of locomotor cpg: insights from a simple neuromechanical model. Ann N Y Acad Sci 1198:21–34

    PubMed  Article  Google Scholar 

  21. Matsubara T, Morimoto J, Nakanishi J, aki Sato M, Doya K (2006) Learning cpg-based biped locomotion with a policy gradient method. Robot Auton Syst 54(11):911–920

    Article  Google Scholar 

  22. Matsuoka K (1985) Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biol Cybern 52(6):367–376

    CAS  PubMed  Article  Google Scholar 

  23. McCrea DA, Rybak IA (2008) Organization of mammalian locomotor rhythm and pattern generation. Brain Res Rev 57(1):134–146

    PubMed Central  PubMed  Article  Google Scholar 

  24. Miyakoshi S, Taga G, Kuniyoshi Y, Nagakubo A (1998) Three dimensional bipedal stepping motion using neural oscillators-towards humanoid motion in the real world. Intelligent Robots and Systems. Proceedings., 1998 IEEE/RSJ international conference on, vol 1. IEEE, pp 84–89 (1998)

  25. Nassour J, Henaff P, Ouezdou FB, Cheng G (2009) Experience-based learning mechanism for neural controller adaptation: application to walking biped robots. In 2009 IEEE/RSJ international conference on intelligent robots and systems, October 11–15, St. Louis, MO, USA, pp 2616–2621

  26. Nassour J, Hugel V, Ouezdou F, Cheng G (2013) Qualitative adaptive reward learning with success failure maps: applied to humanoid robot walking. IEEE Trans Neural Netw Learn Syst 24(1):81–93

    Google Scholar 

  27. Orlovsky G, Deliagina T, Grillner S (1999) Neuronal control of locomotion from mollusc to man. Oxford University Press, Oxford

    Book  Google Scholar 

  28. Perret C, Cabelguen J, Orsal D (1988) Stance and motion: facts and concepts. Plenum Press, New York, ch. Analysis of the pattern of activity in “knee flexor” motoneurons during locomotion in the cat, pp 133–141

  29. Purves D, Augustine GJ, Fitzpatrick D, Hall WC, Lamantia A-S, McNamara JO, Williams SM (2004) Neuroscience, 3rd edn. Sinauer Associates Inc, Sunderland

  30. Righetti L, Ijspeert AJ (2006) Programmable central pattern generators: an application to biped locomotion control. In: Proceedings of the 2006 iEEE international conference on robotics and automation, pp 1585–1590

  31. Rossignol S, Dubuc R, Gossard J-P (2006) Dynamic sensorimotor interactions in locomotion. Physiol Rev 86(1):89–154

    PubMed  Article  Google Scholar 

  32. Rowat P, Selverston A (1991) Learning algorithms for oscillatory networks with gap junctions and membrane currents. Netw Comput Neural Syst 2(1):17–41

    Article  Google Scholar 

  33. Rowat PF, Selverston AI (1997) Oscillatory mechanisms in pairs of neurons connected with fast inhibitory synapses. J Comput Neurosci 4(2):103–127

    CAS  PubMed  Article  Google Scholar 

  34. Rybak IA, Shevtsova NA, Lafreniere-Roula M, McCrea DA (2006) Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. J Physiol 577:617–639

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  35. Shik M, Orlovsky G, Severin F (1966) Organization of locomotor synergism. Biofizika 11(5):879–886

    CAS  PubMed  Google Scholar 

  36. Shik ML, Severin FV, Orlovskiĭ GN (1966) Control of walking and running by means of electric stimulation of the midbrain. Biofizika 11(4):659–666

    CAS  PubMed  Google Scholar 

  37. Swinnen SP, Vangheluwe S, Wagemans J, Coxon JP, Goble DJ, Impe AV, Sunaert S, Peeters RR, Wenderoth N (2010) Shared neural resources between left and right interlimb coordination skills: the neural substrate of abstract motor representations. NeuroImage 49(3):2570–2580

    CAS  PubMed  Article  Google Scholar 

  38. Taga G, Yamaguchi Y, Shimizu H (1991) Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol Cybern 65(3):147–159

    CAS  PubMed  Article  Google Scholar 

  39. Wadden T, Ekeberg O (1998) A neuro-mechanical model of legged locomotion: single leg control. Biol Cybern 79(2):161–173

    CAS  PubMed  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to John Nassour.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 4221 KB)

Supplementary material 2 (avi 11440 KB)

Supplementary material 3 (mp4 6429 KB)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Nassour, J., Hénaff, P., Benouezdou, F. et al. Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots. Biol Cybern 108, 291–303 (2014).

Download citation


  • Central pattern generator
  • Robot locomotion
  • Humanoid walking