Advertisement

Biological Cybernetics

, Volume 108, Issue 1, pp 103–119 | Cite as

Visually guided gait modifications for stepping over an obstacle: a bio-inspired approach

  • Pedro Silva
  • Vitor Matos
  • Cristina P. SantosEmail author
Original Paper

Abstract

There is an increasing interest in conceiving robotic systems that are able to move and act in an unstructured and not predefined environment, for which autonomy and adaptability are crucial features. In nature, animals are autonomous biological systems, which often serve as bio-inspiration models, not only for their physical and mechanical properties, but also their control structures that enable adaptability and autonomy—for which learning is (at least) partially responsible. This work proposes a system which seeks to enable a quadruped robot to online learn to detect and to avoid stumbling on an obstacle in its path. The detection relies in a forward internal model that estimates the robot’s perceptive information by exploring the locomotion repetitive nature. The system adapts the locomotion in order to place the robot optimally before attempting to step over the obstacle, avoiding any stumbling. Locomotion adaptation is achieved by changing control parameters of a central pattern generator (CPG)-based locomotion controller. The mechanism learns the necessary alterations to the stride length in order to adapt the locomotion by changing the required CPG parameter. Both learning tasks occur online and together define a sensorimotor map, which enables the robot to learn to step over the obstacle in its path. Simulation results show the feasibility of the proposed approach.

Keywords

Adaptive robot controller Autonomy in robotics Biological inspiration Sensorimotor map Forward internal model Real-time learning 

Notes

Acknowledgments

We thank Keir Pearson, Arthur Prochazka and Trevor Drew for their suggestions related to the work. This work is funded by FEDER Funding supported by the Operational Program Competitive Factors—COMPETE and National Funding supported by the FCT—Portuguese Science Foundation through projects PEst-OE/EEI/LA0009/2011 and PTDC/EEACRO/100655/ 2008. Pedro Silva is supported by Grant CRO-BI-2012(2), and Vitor Matos is supported by SFRH/BD/62047/2009.

Supplementary material

Supplementary material 1 (mpg 8298 KB)

References

  1. Aoi S, Tsuchiya K (2005) Locomotion control of a biped robot using nonlinear oscillators. Auton Robots 19(3):219–232CrossRefGoogle Scholar
  2. Aoi S, Tsuchiya K (2007) Adaptive behavior in turning of an oscillator-driven biped robot. Auton Robots 23(1):37–57CrossRefGoogle Scholar
  3. Albiez, J, Ilg W, Luksch T, Berns K, Dillmann R (2001) Learning a reactive posture control on the four-legged walking machine bisam. In: IEEE/RSJ international conference on intelligent robots and systems, 2001. Proceedings, vol 2, pp 999–1004. IEEEGoogle Scholar
  4. Aoi S, Sasaki H, Kazuo TA (2007) Multilegged modular robot that meanders: investigation of turning maneuvers using its inherent dynamic characteristics. SIAM J Appl Dyn Syst 6(2):348–377CrossRefGoogle Scholar
  5. Aoi S, Ogihara N, Funato T, Sugimoto Y, Tsuchiya K (2010a) Evaluating functional roles of phase resetting in generation of adaptive human bipedal walking with a physiologically based model of the spinal pattern generator. Biol Cybern 102(5):373–387PubMedCrossRefGoogle Scholar
  6. Aoi S, Yamashita T, Ichikawa A, Tsuchiya K (2010b) Hysteresis in gait transition induced by changing waist joint stiffness of a quadruped robot driven by nonlinear oscillators with phase resetting. In: Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1915–1920Google Scholar
  7. Aoi S, Fujiki S, Yamashita T, Kohda T, Senda K, Tsuchiya K (2011b) Generation of adaptive splitbelt treadmill walking by a biped robot using nonlinear oscillators with phase resetting. In: Proceedings of the 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2274–2279Google Scholar
  8. Berns K, Ilg W, Deck M, Dillmann R (2008) Adaptive control of the four-legged walking machine BISAM. In: Proceedings of the 1998 IEEE international conference on control applications, vol 1, pp 428–432Google Scholar
  9. Brown TG (1911) The intrinsic factors in the act of progression in the mammal. In: Proceedings of the Royal Society of London. Series B, containing papers of a biological character, vol 84, pp 308–319Google Scholar
  10. Buchli J, Ijspeert AJ (2008) Self-organized adaptive legged locomotion in a compliant quadruped robot. Auton Robots 25:331–347CrossRefGoogle Scholar
  11. Burke RE (2007) Sir Charles Sherrington’s The integrative action of the nervous system: a centenary appreciation. Gait Brian 130(4):887–894CrossRefGoogle Scholar
  12. Büschges A, Borgmann A (2013) Network modularity: back to the future in motor control. Curr Biol 23(29):R936–R938PubMedCrossRefGoogle Scholar
  13. Cruse H, Kindermann T, Schumm M, Dean J, Schmitz J (1998) Walknet–a biologically inspired network to control six-legged walking. Neural Netw 11(7):1435–1447PubMedCrossRefGoogle Scholar
  14. Doshi F, Brunskill E, Shkolnik A, Kollar T, Rohanimanesh K, Tedrake R, Roy N (2007) Collision detection in legged locomotion using supervised learning. In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007, pp 317–322, Oct 2007Google Scholar
  15. Drew T, Andujar J-E, Lajoie K, Yakovenko S (2008) Cortical mechanisms involved in visuomotor coordination during precision walking. Brain Res Rev 57:199–211PubMedCrossRefGoogle Scholar
  16. 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, New Orleans, LA, USA, 26 April–1 May, pp 3036–3042Google Scholar
  17. Endo G, Nakanishi J, Morimoto J, Cheng G (2005) Experimental studies of a neural oscillator for biped locomotion with QRIO. In: Proceedings of the 2005 IEEE international conference on robotics and automation, ICRA 2005, pp 596–602Google Scholar
  18. Forssberg H (1979) Stumbling corrective reaction: a phase-dependent compensatory reaction during locomotion. J Neurophysiol 42(4):936–953PubMedGoogle Scholar
  19. Fukuoka Y, Kimura H, Cohen A (2003) Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts. Int J Robot Res 22(3–4):187CrossRefGoogle Scholar
  20. 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(3):243–259CrossRefGoogle Scholar
  21. 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): 263–273Google Scholar
  22. Gritsenko V, Yakovenko S, Kalaska JF (2009) From integration of predictive feedforward and sensory feedback signals for online control of visually guided movement. J Neurophysiol 102:914–930PubMedCrossRefGoogle Scholar
  23. Held R (1961) Sensory deprivation: facts in search of a theory. Exposure-history as a factor in maintaining stability of perception and coordination. J Nerv Ment Dis 132:26–32Google Scholar
  24. Heliot R, Espiau B (2008) Multisensor input for cpg-based sensory—motor coordination. IEEE Trans Robot 24(1):191–195CrossRefGoogle Scholar
  25. Hoffmann H (2007) Perception through visuomotor anticipation in a mobile robot. Neural Netw 20(1):22–33PubMedCrossRefGoogle Scholar
  26. Ijspeert A (2008) special issue: Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21(4):642–653PubMedCrossRefGoogle Scholar
  27. Ilg W, Albiez J, Jedele H, Berns K, Dillmann R (1999) Adaptive periodic movement control for the four legged walking machine bisam. In: IEEE international conference on robotics and automation. Proceedings, vol 3, pp 2354–2359. IEEEGoogle Scholar
  28. Ishii T, Masakado S, Ishii K (2004) Locomotion of a quadruped robot using CPG. In: Proceedings in 2004 IEEE international joint conference on neural networks, vol 4, pp 3179–3184Google Scholar
  29. Kalakrishnan M, Buchli J, Pastor P, Mistry M, Schaal S (2010) Fast, robust quadruped locomotion over challenging terrain. In: IEEE international conference on robotics and automation (ICRA), 2010, pp 2665–2670. IEEEGoogle Scholar
  30. Kiehn O (2006) Locomotor circuits in the mammalian spinal cord. Annu Rev Neurosci 29(1):279–306PubMedCrossRefGoogle Scholar
  31. Kimura H, Fukuoka Y (2004) Biologically inspired adaptive dynamic walking in outdoor environment using a self-contained quadruped robot: ‘Tekken2’. In: Proceedings. 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004. (IROS 2004), vol 1, pp 986–991Google Scholar
  32. Kimura H, Fukuoka Y, Cohen A (2007a) Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts. Int J Robot Res 26(5):475CrossRefGoogle Scholar
  33. Kimura H, Fukuoka Y, Cohen AH (2007b) Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts. Int J Robot Res 26(5):475–490CrossRefGoogle Scholar
  34. Komatsu T, Usui M (2005) Dynamic walking and running of a bipedal robot using hybrid central pattern generator method. In: Proceedings of the 2005 IEEE international conference mechatronics and automation, vol 2, pp 987–992Google Scholar
  35. Lee DN, Lishman JR, Thomson JA (1982) Regulation of gait in long jumping. J Exp Psychol Hum Percept Perform 8(3):448CrossRefGoogle Scholar
  36. Lewis M (2002) Detecting surface features during locomotion using optic flow. In: IEEE international conference on robotics and automation, 2002. Proceedings. ICRA ’02, vol 1, pp 305–310Google Scholar
  37. Lewis M, Simó L (1999) Elegant stepping: a model of visually triggered gait adaptation. Connect Sci 11(3):331–344CrossRefGoogle Scholar
  38. Lewis MA, Simó LS (2001) Certain principles of biomorphic robots. Auton Robots 11(3):221–226CrossRefGoogle Scholar
  39. Lewis M, Bekey G (2002) Gait adaptation in a quadruped robot. Auton Robots 12(3):301–312CrossRefGoogle Scholar
  40. Maes P, Brooks R (1990) Learning to coordinate behaviors. In: Proceedings of the eighth national conference on artificial intelligence, pp 796–802Google Scholar
  41. Manoonpong P, Wörgötter F (2009) Efference copies in neural control of dynamic biped walking. Robot Auton Syst 57(11):1140–1153CrossRefGoogle Scholar
  42. 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
  43. Manoonpong P, Parlitz U, Wörgötter F (2013) Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines. Front Neural Circuits 7:12 doi 10.339/fncri.2013.00012Google Scholar
  44. Marder E, Bucher D, Schulz DJ, Taylor AL (2005) Invertebrate central pattern generation moves along. Curr Biol 15(17):R685–R699PubMedCrossRefGoogle Scholar
  45. Matos V, Santos C (2011) Omnidirectional locomotion in a quadruped robot: a cpg-based approach. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), 2010, pp 3392–3397. IEEEGoogle Scholar
  46. Matsubara T, Morimoto J, Nakanishi J, Sato M, Doya K (2005) Learning CPG-based biped locomotion with a policy gradient method. In: Proceedings of the 2005 5th IEEE-RAS international conference on humanoid robots, pp 208–213Google Scholar
  47. Maufroy C, Nishikawa T, Kimura H (2010a) Stable dynamic walking of a quadruped robot Kotetsu; using phase modulations based on leg loading/unloading. In: Proceedings of the 2010 IEEE international conference on robotics and automation, ICRA 2010, pp 5225–5230Google Scholar
  48. Maufroy C, Kimura H, Takase K (2010b) Integration of posture and rhythmic motion controls in quadrupedal dynamic walking using phase modulations based on leg loading/unloading. Auton Robots 28(3):331–353CrossRefGoogle Scholar
  49. McVea D, Pearson K (2007a) Contextual learning and obstacle memory in the walking cat. Integr Comp Biol 47(4):457–464PubMedCrossRefGoogle Scholar
  50. McVea DA, Pearson KG (2007b) Long-lasting, context-dependent modification of stepping in the cat after repeated stumbling-corrective responses. J Neurophysiol 97(1):659–669PubMedCrossRefGoogle Scholar
  51. Miall R, Wolpert DM (1996) Forward models for physiological motor control. Neural Netw 9(8):1265–1279PubMedCrossRefGoogle Scholar
  52. Michel O (2004) Webots: professional mobile robot simulation. J Adv Robot Syst 1(1):39–42Google Scholar
  53. Morimoto J, Hyon S, Atkeson CG, Cheng G (2008a) Low-dimensional feature extraction for humanoid locomotion using kernel dimension reduction. In: Proceedings of the 2008 IEEE international conference on robotics and automation, ICRA 2008, pp 2711–2716Google Scholar
  54. Morimoto J, Endo G, Nakanishi J, Cheng GA (2008b) Biologically inspired biped locomotion strategy for humanoid robots: modulation of sinusoidal patterns by a coupled oscillator model. IEEE Trans Robot 24(1):185–191CrossRefGoogle Scholar
  55. Ogino M, Katoh Y, Aono M, Asada M, Hosoda K (2004) Reinforcement learning of humanoid rhythmic walking parameters based on visual information. Adv Robot 18(7):677–697CrossRefGoogle Scholar
  56. Orlovskii GN, Deliagina TG, Grillner S (1999) Neuronal control of locomotion: from mollusc to man. Oxford University Press, OxfordCrossRefGoogle Scholar
  57. Pastor P, Kalakrishnan M, Chitta S, Theodorou E, Schaal S (2011a) Skill learning and task outcome prediction for manipulation. In: IEEE international conference on robotics and automation (ICRA), pp 3828–3834. IEEEGoogle Scholar
  58. Pastor P, Righetti L, Kalakrishnan M, Schaal S (2011b) Online movement adaptation based on previous sensor experiences. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 365–371. IEEEGoogle Scholar
  59. Pearson K (2004) Generating the walking gait: role of sensory feedback. Brain mechanisms for the integration of posture and movement, vol 143, Elsevier, Amsterdam, pp 123–129Google Scholar
  60. Prochazka A (2002) The man-machine analogy in robotics and neurophysiology. J Autom Control 12:4–8CrossRefGoogle Scholar
  61. Prochazka A, Gritsenko V, Yakovenko S (2002) Sensory control of locomotion: reflexes versus higher-level control. Sensori-motor control, vol 57. Kluwer, New YorkGoogle Scholar
  62. Righetti L, Ijspeert A (2008) Pattern generators with sensory feedback for the control of quadruped locomotion. In: IEEE international conference on robotics and automation, 2008. ICRA 2008, pp 819–824, May 2008Google Scholar
  63. Rossignol S, Dubuc R, Gossard J-P (2006) Dynamic sensorimotor interactions in locomotion. Physiol Rev 86(1):89–154Google Scholar
  64. Santos CP, Matos V (2012) Cpg modulation for navigation and omnidirectional quadruped locomotion. Robot Auton Syst 60(6):912–927CrossRefGoogle Scholar
  65. Schenck W, Möller R (2007) Training and application of a visual forward model for a robot camera head. In: Butz MV, Sigaud O, Pezzulo G, Baldassarre G Anticipatory behavior in adaptive learning systems, pp 153–169. Springer, BerlinGoogle Scholar
  66. Schröder-Schetelig J, Manoonpong P, Wörgötter F (2010), Using efference copy and a forward internal model for adaptive biped walking. Auton Robots 29:1–10 Google Scholar
  67. Shimada S, Egami T, Ishimura K, Wada M (2002) Neural control of quadruped robot for autonomous walking on soft terrain. In: Asama H, Arai T, Fukuda T, Hasegawa T (eds) Distributed autonomous robotic systems, vol 5. Springer, Japan, pp 415–423Google Scholar
  68. Silva P, Matos V, Santos CP (2012) Adaptive quadruped locomotion: learning to detect and avoid an obstacle. In: Ziemke T, Balkenius C, Hallam J (eds) From animals to animats, vol 12. Springer, pp 361–370Google Scholar
  69. Sousa J, Matos V, Peixoto dos Santos C (2010) A bio-inspired postural control for a quadruped robot: an attractor-based dynamics. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 5329–5334. IEEEGoogle Scholar
  70. Sugimoto N, Morimoto J (2011) Phase-dependent trajectory optimization for CPG-based biped walking using path integral reinforcement learning. In: Proceedings of the 11th IEEE-RAS international conference on humanoid robots, pp 255–260Google Scholar
  71. Sutton R, Barto A (1998) Reinforcement learning: An introduction, vol 1. Cambridge University Press, CambridgeGoogle Scholar
  72. Taga G, Yamaguchi Y, Shimizu H (1991) Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol Cybern 65(3):147–159Google Scholar
  73. Taga G (1995a) A model of the neuro-musculo-skeletal system for human locomotion–I. Emergence of basic gait. Biol Cybern 73(2):97–111PubMedCrossRefGoogle Scholar
  74. Taga G (1995b) A model of the neuro-musculo-skeletal system for human locomotion—II. Real-time adaptability under various constraints. Biol Cybern 73(2):113–121PubMedCrossRefGoogle Scholar
  75. Taga G (1998) A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance. Biol Cybern 78(1):9–17PubMedCrossRefGoogle Scholar
  76. Takemura H, Deguchi M, Ueda J, Matsumoto Y, Ogasawara T (2005) Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Slip-adaptive walk of quadruped robot. Robot Auton Syst 53:124–141CrossRefGoogle Scholar
  77. Takemura H, Ueda J, Matsumoto Y, Ogasawara T (2002) A study of a gait generation of a quadruped robot based on rhythmic control–optimization of CPG parameters by a fast dynamics simulation environment. In: Proceedings of 5th international conference on climbing and walking robots (CLAWAR 2002), pp 759–766Google Scholar
  78. von Holst E, Mittelstaedt H (1950) Das Reafferenzprinzip. Naturwissenschaften 37(20):464–476CrossRefGoogle Scholar
  79. Wilkinson EJ, Sherk HA (2005) The use of visual information for planning accurate steps in a cluttered environment. Behav Brain Res 164(2):270–274PubMedCrossRefGoogle Scholar
  80. Wolpert D, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11(7–8):1317–1329PubMedCrossRefGoogle Scholar
  81. Yakovenko S, Gritsenko V, Prochazka A (2004) Contribution of stretch reflexes to locomotor control: a modeling study. Biol Cybern 90(2):146-155Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Centro AlgoritmiUniversity of MinhoBragaPortugal

Personalised recommendations