Advertisement

MIRO: A Robot “Mammal” with a Biomimetic Brain-Based Control System

  • Ben Mitchinson
  • Tony J. Prescott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9793)

Abstract

We describe the design of a novel commercial biomimetic brain-based robot, MIRO, developed as a prototype robot companion. The MIRO robot is animal-like in several aspects of its appearance, however, it is also biomimetic in a more significant way, in that its control architecture mimics some of the key principles underlying the design of the mammalian brain as revealed by neuroscience. Specifically, MIRO builds on decades of previous work in developing robots with brain-based control systems using a layered control architecture alongside centralized mechanisms for integration and action selection. MIRO’s control system operates across three core processors, P1-P3, that mimic aspects of spinal cord, brainstem, and forebrain functionality respectively. Whilst designed as a versatile prototype for next generation companion robots, MIRO also provides developers and researchers with a new platform for investigating the potential advantages of brain-based control.

Keywords

Control Architecture Layered Control Sensory Stream Robot Companion Shaft Encoder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The development of the MIRO robot was funded by Eaglemoss Publishing and Consequential Robotics with contributions from Sebastian Conran, Tom Pearce, Victor Chen, Dave Keating, Jim Wyatt, Maggie Calmels, and Emily Collins. Our research on layered architectures was also supported by the FP7 WYSIWYD project (ICT-612139), and the EPSRC BELLA project (EP/I032533/1).

References

  1. 1.
    Prescott, T.J., et al.: Embodied Models and Neurorobotics. In: Arbib, M.A., Bonaiuto, J.J. (eds.) From Neuron to Cognition via Computational Neuroscience. MIT Press, Cambridge (in press)Google Scholar
  2. 2.
    Floreano, D., Auke, J., Ijspeert, J., Schaal, S.: Robotics and neuroscience. Curr. Biol. 24(18), R910–R920 (2014)CrossRefGoogle Scholar
  3. 3.
    Prescott, T.J., Ibbotson, C.: A robot trace-maker: modeling the fossil evidence of early invertebrate behavior. Artif Life 3, 289–306 (1997)CrossRefGoogle Scholar
  4. 4.
    Prescott, T.J., et al.: A robot model of the basal ganglia: behaviour and intrinsic processing. Neural Netw. 19(1), 31–61 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Pearson, M.J., et al.: Biomimetic vibrissal sensing for robots. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366(1581), 3085–3096 (2011)CrossRefGoogle Scholar
  6. 6.
    Lepora, N.F., et al.: Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination. J. R. Soc. Interface 9(72), 1517–1528 (2012)CrossRefGoogle Scholar
  7. 7.
    Mitchinson, B., et al.: Biomimetic tactile target acquisition, tracking and capture. Robot. Auton. Syst. 62(3), 366–375 (2014)CrossRefGoogle Scholar
  8. 8.
    Collins, E.C., Prescott, T.J., Mitchinson, B.: Saying it with light: a pilot study of affective communication using the MIRO robot. In: Wilson, S.P., Verschure, P.F.M.J., Mura, A., Prescott, T.J. (eds.) Living Machines 2015. LNCS, vol. 9222, pp. 243–255. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  9. 9.
    Collins, E.C., et al.: MIRO: a versatile biomimetic edutainment robot. In: 12th Conference on Advances in Computer Entertainment, Iskandar, Malaysia (2015)Google Scholar
  10. 10.
    Prescott, T.J.: Forced moves or good tricks in design space? landmarks in the evolution of neural mechanisms for action selection. Adapt. Behav. 15(1), 9–31 (2007)CrossRefGoogle Scholar
  11. 11.
    Hinde, R.A.: Animal Behaviour: a Synthesis of Ethology and Comparative Psychology. McGraw-Hill, London (1966)Google Scholar
  12. 12.
    McFarland, D., Bosser, T.: Intelligent Behaviour in Animals and Robots. MIT Press, Cambridge (1993)Google Scholar
  13. 13.
    Verschure, P.F.M.J., Krose, B., Pfeifer, R.: Distributed adaptive control: the self-organization of structured behavior. Robot. Auton. Syst. 9, 181–196 (1992)CrossRefGoogle Scholar
  14. 14.
    Arbib, M.A., Liaw, J.S.: Sensorimotor transformations in the worlds of frogs and robots. Artif. Intell. 72(1–2), 53–79 (1995)CrossRefGoogle Scholar
  15. 15.
    Prescott, T.J., Redgrave, P., Gurney, K.N.: Layered control architectures in robots and vertebrates. Adapt. Behav. 7(1), 99–127 (1999)CrossRefGoogle Scholar
  16. 16.
    Jackson, J.H.: Evolution and dissolution of the nervous system. In: Taylor, J. (ed.) Selected Writings of John Hughlings Jackson. Staples Press, London (1884/1958)Google Scholar
  17. 17.
    Prescott, T.J.: Layered control architectures. In: Pashler, H. (ed.) Encyclopedia of Mind, pp. 464–467. Sage, London (2013)Google Scholar
  18. 18.
    Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA-2, 14–23 (1986)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Penfield, W.: Centrencephalic integrating system. Brain 81, 231–234 (1958)CrossRefGoogle Scholar
  20. 20.
    Humphries, M.D., Gurney, K., Prescott, T.J.: Is there a brainstem substrate for action selection? Philos. Trans. R. Soc. Lond. B Biol. Sci. 362(1485), 1627–1639 (2007)CrossRefGoogle Scholar
  21. 21.
    Prescott, T.J., et al.: Whisking with robots: From rat vibrissae to biomimetic technology for active touch. IEEE Robot. Autom. Mag. 16(3), 42–50 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Redgrave, P., Prescott, T., Gurney, K.N.: The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89, 1009–1023 (1999)CrossRefGoogle Scholar
  23. 23.
    Brooks, R.A.: New approaches to robotics. Science 253, 1227–1232 (1991)CrossRefGoogle Scholar
  24. 24.
    Gandhi, N.J., Katnani, H.A.: Motor functions of the superior colliculus. Annu. Rev. Neurosci. 34, 205–231 (2011)CrossRefGoogle Scholar
  25. 25.
    Fox, C., et al.: Technical integration of hippocampus, basal ganglia and physical models for spatial navigation. Front. Neuroinformatics, 3(6) (2009)Google Scholar
  26. 26.
    Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)CrossRefGoogle Scholar
  27. 27.
    Hofe, R., Moore, R.K.: Towards an investigation of speech energetics using ‘AnTon’: an animatronic model of a human tongue and vocal tract. Connection Sci. 20(4), 319–336 (2008)CrossRefGoogle Scholar
  28. 28.
    Mitchinson, B., Prescott, T.J.: Whisker movements reveal spatial attention: a unified computational model of active sensing control in the rat. PLoS Comput. Biol. 9(9), e1003236 (2013)CrossRefGoogle Scholar
  29. 29.
    Jeffress, L.A.: A place theory of sound localization. J Comp Physiol Psychol. 41(1), 35–39 (1948)CrossRefGoogle Scholar
  30. 30.
    Mitchinson, B.: Attention and orienting. In: Lepora, P.T.J.N., Verschure, P.F.M.J. (eds.) Living Machines: A Handbook of Research in Biomimetic and Biohybrid Systems OUP: Oxford (in press)Google Scholar
  31. 31.
    Prescott, T.J., et al.: The robot vibrissal system: understanding mammalian sensorimotor co-ordination through biomimetics. In: Krieger, P., Groh, A. (eds.) Sensorimotor Integration in the Whisker System, pp. 213–240. Springer, New York (2015)CrossRefGoogle Scholar
  32. 32.
    Higgins, W.T.: A comparison of complementary and Kalman filtering. IEEE Trans. Aerosp. Electron. Syst. AES-11(3), 321–325 (1975)CrossRefGoogle Scholar
  33. 33.
    Burr, D.C., Morrone, M.C., Ross, J.: Selective suppression of the magnocellular visual pathway during saccadic eye movements. Nature 371(6497), 511–513 (1994)CrossRefGoogle Scholar
  34. 34.
    Redgrave, P., Prescott, T.J., Gurney, K.: Is the short-latency dopamine response too short to signal reward error?. Trends Neurosci. 22(4), 146–151 (1999)CrossRefGoogle Scholar
  35. 35.
    Thorpe, S.J.: The speed of categorization in the human visual system. Neuron 62(2), 168–170 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Psychology and Sheffield RoboticsUniversity of SheffieldSheffieldUK

Personalised recommendations