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Biological Cybernetics

, Volume 108, Issue 3, pp 291–303 | Cite as

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

  • John NassourEmail author
  • Patrick Hénaff
  • Fethi Benouezdou
  • Gordon Cheng
Original Paper

Abstract

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.

Keywords

Central pattern generator Robot locomotion Humanoid walking 

Supplementary material

Supplementary material 1 (mp4 4221 KB)

Supplementary material 2 (avi 11440 KB)

Supplementary material 3 (mp4 6429 KB)

References

  1. Amrollah E, Henaff P (2010) On the role of sensory feedbacks in rowat-selverston cpg to improve robot legged locomotion. Front Neurorobot 4:00113CrossRefGoogle Scholar
  2. Brown GT (1911) The intrinsic factors in the act of progression in the mammal. Proc R Soc Lond 84(572):308–319CrossRefGoogle 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):18–46PubMedCentralPubMedGoogle Scholar
  4. Choi JT, Bastian AJ (2007) Adaptation reveals independent control networks for human walking. Nat Neurosci 10(8):1055–1062PubMedCrossRefGoogle 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):790–797CrossRefGoogle 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):155–181CrossRefGoogle 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 27:213–228CrossRefGoogle 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 3036–3042Google Scholar
  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 25:243–259CrossRefGoogle 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):263–273PubMedCrossRefGoogle 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):347–356PubMedCrossRefGoogle 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, FranceGoogle Scholar
  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):1416–1420PubMedCrossRefGoogle 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):162–173PubMedGoogle 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):1120–1132PubMedCrossRefGoogle 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 1019–1022Google Scholar
  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):264–269CrossRefGoogle 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):e134PubMedCentralPubMedCrossRefGoogle Scholar
  19. Marder E, Bucher D (2001) Central pattern generators and the control of rhythmic movements. Curr Biol 11(23):R986–R996PubMedCrossRefGoogle 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 1198:21–34PubMedCrossRefGoogle 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):911–920CrossRefGoogle Scholar
  22. Matsuoka K (1985) Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biol Cybern 52(6):367–376PubMedCrossRefGoogle Scholar
  23. McCrea DA, Rybak IA (2008) Organization of mammalian locomotor rhythm and pattern generation. Brain Res Rev 57(1):134–146PubMedCentralPubMedCrossRefGoogle 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)Google Scholar
  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 2616–2621Google Scholar
  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):81–93Google Scholar
  27. Orlovsky G, Deliagina T, Grillner S (1999) Neuronal control of locomotion from mollusc to man. Oxford University Press, OxfordCrossRefGoogle 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 133–141Google Scholar
  29. Purves D, Augustine GJ, Fitzpatrick D, Hall WC, Lamantia A-S, McNamara JO, Williams SM (2004) Neuroscience, 3rd edn. Sinauer Associates Inc, SunderlandGoogle Scholar
  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 1585–1590Google Scholar
  31. Rossignol S, Dubuc R, Gossard J-P (2006) Dynamic sensorimotor interactions in locomotion. Physiol Rev 86(1):89–154PubMedCrossRefGoogle Scholar
  32. Rowat P, Selverston A (1991) Learning algorithms for oscillatory networks with gap junctions and membrane currents. Netw Comput Neural Syst 2(1):17–41CrossRefGoogle Scholar
  33. Rowat PF, Selverston AI (1997) Oscillatory mechanisms in pairs of neurons connected with fast inhibitory synapses. J Comput Neurosci 4(2):103–127PubMedCrossRefGoogle 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 577:617–639PubMedCentralPubMedCrossRefGoogle Scholar
  35. Shik M, Orlovsky G, Severin F (1966) Organization of locomotor synergism. Biofizika 11(5):879–886PubMedGoogle 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):659–666PubMedGoogle 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):2570–2580PubMedCrossRefGoogle 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):147–159PubMedCrossRefGoogle Scholar
  39. Wadden T, Ekeberg O (1998) A neuro-mechanical model of legged locomotion: single leg control. Biol Cybern 79(2):161–173PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • John Nassour
    • 1
    Email author
  • Patrick Hénaff
    • 2
  • Fethi Benouezdou
    • 3
  • Gordon Cheng
    • 1
  1. 1.Institute for Cognitive Systems (ICS)Technical University of Munich (TUM)MunichGermany
  2. 2.LORIA, UMR 7503, University of Lorraine-INRIA-CNRSUniversity of LorraineNancyFrance
  3. 3.The Engineering System Laboratory (LISV)Versailles University (UVSQ)VersaillesFrance

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