Abstract
A newborn foal can learn to walk soon after birth through a process of rapid adaptation acting on its locomotor controller. It is proposed here that this kind of adaptation can be modeled as a distributed system of adaptive modules (AMs) acting on a distributed system of adaptive oscillators called Adaptive Ring Rules (ARRs), augmented with appropriate and simple reflexes. It is shown that such a system can self-program through interaction with the environment. The adaptation emerges spontaneously as several discrete stages: Body twisting, short quick steps, and finally longer, coordinated stepping.
This approach is demonstrated on a quadrupedal robot. The result is that the system can learn to walk several minutes after inception.
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Bay, J.S. and Hemami, H. 1987. Modeling of a neural pattern generator with coupled nonlinear oscillators. IEEE Trans. on Biomedical Engineering, BME-34(4):297–306.
Bekoff, A. 1985. Development of locomotion in vertebrates, a comparative perspective. The Comparative Development of Adaptive Skills: Evolutionary Implications, E.S. Gallin (Ed.), Lawrence Erlbaum Assoc.: London, pp. 57–94.
Carrier, D. 1990. Activity of the hypaxial muscles during walking in the lizard Iguana Iguana. J. Exp. Biol., 152:453–470.
Carrier, D.R. 1993. Action of the hypaxial muscles during walking and swimming in the salamander Dicamptodon Ensatus. J. Exp. Biol., 180:75–83.
Cohen, A.H. 1988. Evolution of the vertebrate central pattern generator for locomotion. Neural Control of Rhythmic Movements in Vertebrates, S.R.A.H. Cohen and S. Grillner (Eds.), Wiley: New York.
Cohen, A.H., Holmes, P.J. et al. 1982. The nature of the coupling between segmental oscillators of the lamprey spinal generator for locomotion: A mathematical model. J. Math. Biol., 13:345–369.
Collins, J.J. and Stewart, I.N. 1993. Coupled nonlinear oscillators and the symmetries of animal gaits. J. Nonlinear Sci., 3:349–392.
Grillner, S. and Wallén, P. 1985. Central pattern generators for locomotion, with special reference to vertebrates. Annual Review of Neuroscience, 8:233–261.
Grillner, S. and Zangger, P. 1975. How detailed is the central pattern generation for locomotion? Brain Research, 88(2):367–371.
Ilg, W. and Berns, K. 1995. A learning architecture based on reinforcement learning for adaptive control of the walking machine LAURON. Robotics and Autonomous Systems, 15:321–334.
Ito, S., Yuasa, H. et al. 1998. A mathematical model of adaptive behavior in quadruped locomotion. Bio Cybern., 78:337–347.
Jalics, L., Hemami, H. et al. 1997. A control strategy for terrain adaptive bipedal locomotion. Autonomous Robots, 4(3):243–257.
Kawato, M. and Wolpert, D. 1998. Internal models for motor control. Novartis Found Symp, 218:291–304; discussion 304–307.
Kimura, H., Akiyama, S. et al. 1999. Realization of dynamicwalking and running of the quadruped using neural oscillator. Autonomous Robots, 7(3).
Kotliar, B.I., Maorov, V.I. et al. 1975. Models of learning based on the plastic properties of the placing reaction in cats. Zh Vyssh Nerv Deiat, 25(5):967–973.
Lewis, M.A. 1996. Self-organization of locomotory controllers in robots and animals, Ph.D. Dissertation. Department of Electrical Engineering. Los Angeles, University of Southern California.
Lewis, M.A., Etienne-Cummings, R. et al. 2000. Toward biomorphic control using custom aVLSI chips. 2000 International Conference on Robotics and Automation, San Francisco, IEEE.
Lewis, M.A., Fagg, A.H. et al. 1992. Genetic programming approach to the construction of a neural network for control of a walking robot. 1992 IEEE International Conference on Robotics and Automation, Nice, France.
Lewis, M.A., Hartmann, M. et al. 2001. Control of a robot leg with an adaptive aVLSI CPG chip. Neurocomputing (Procedings of the Computational Neuroscience Meeting), 38–40:1409–1421.
Lewis, M.A. and Simó, L.S. 1999. Elegant stepping: A model of visually triggered gait adaptation. Connection Science, 11(3/4).
Lewis, M.A. and Simó, L.S. 2001. Certain principles of biomorphic robots. Autonomous Robots, 11(3):221–226.
Matsuoka, K. 1987. Mechanisms of frequency and pattern control in the neural rhythm generators. Biol. Cybern., 56:345–353.
Murray, J.D. 1993. Mathematical Biology, Springer-Verlag: Berlin.
Rand, R.H., Cohen, A.H. et al. 1988. Systems of coupled oscillators as models of central pattern generators. Neural Control of Rhythmic Movement in Vertebrates. A.H. Cohen, S. Rossignol, and S. Grillner (Eds.), Wiley: New York.
Sperry, R.W. 1950. Neural basis of the spontaneous optokinetic response produced by visual inversion. Journal of Comparative and Physiological Psychology, 43:482–489.
Taga, G., Yamaguchi, Y. et al. 1991. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol. Cybern., 65:147–159.
Zielinska, T. 1996. Coupled oscillators utilised as gait rhythm generators. Biol. Cybern., 74:263–273.
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Lewis, M.A., Bekey, G.A. Gait Adaptation in a Quadruped Robot. Autonomous Robots 12, 301–312 (2002). https://doi.org/10.1023/A:1015221832567
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DOI: https://doi.org/10.1023/A:1015221832567