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Towards Real-World Neurorobotics: Integrated Neuromorphic Visual Attention

  • Samantha V. Adams
  • Alexander D. Rast
  • Cameron Patterson
  • Francesco Galluppi
  • Kevin Brohan
  • José-Antonio Pérez-Carrasco
  • Thomas Wennekers
  • Steve Furber
  • Angelo Cangelosi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8836)

Abstract

Neuromorphic hardware and cognitive robots seem like an obvious fit, yet progress to date has been frustrated by a lack of tangible progress in achieving useful real-world behaviour. System limitations: the simple and usually proprietary nature of neuromorphic and robotic platforms, have often been the fundamental barrier. Here we present an integration of a mature “neuromimetic” chip, SpiNNaker, with the humanoid iCub robot using a direct AER - address-event representation - interface that overcomes the need for complex proprietary protocols by sending information as UDP-encoded spikes over an Ethernet link. Using an existing neural model devised for visual object selection, we enable the robot to perform a real-world task: fixating attention upon a selected stimulus. Results demonstrate the effectiveness of interface and model in being able to control the robot towards stimulus-specific object selection. Using SpiNNaker as an embeddable neuromorphic device illustrates the importance of two design features in a prospective neurorobot: universal configurability that allows the chip to be conformed to the requirements of the robot rather than the other way ’round, and standard interfaces that eliminate difficult low-level issues of connectors, cabling, signal voltages, and protocols. While this study is only a building block towards that goal, the iCub-SpiNNaker system demonstrates a path towards meaningful behaviour in robots controlled by neural network chips.

Keywords

cognitive robotics attention neuromorphic 

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References

  1. 1.
    Arbib, M., Metta, G., van der Smagt, P.: Neurorobotics: from vision to action. In: Springer Handbook of Robotics, pp. 1453–1480. Springer (2008)Google Scholar
  2. 2.
    Bouganis, A., Shanahan, M.: Training a spiking neural network to control a 4-DOF robotic arm based on spike timing-dependent plasticity. In: Proc. 2010 Int’l Joint Conf. Neural Networks (IJCNN 2010), pp. 4104–4111 (2010)Google Scholar
  3. 3.
    Chersi, F.: Learning Through Imitation: a Biological Approach to Robotics. IEEE Trans. Autonomous Mental Development 4(3), 204–214 (2012)CrossRefGoogle Scholar
  4. 4.
    Furber, S.B., Lester, D.R., Plana, L.A., Garside, J.D., Painkras, E., Temple, S., Brown, A.D.: Overview of the SpiNNaker system architecture. IEEE Trans. Computers PP(99) (2012)Google Scholar
  5. 5.
    Galluppi, F., Brohan, K., Davidson, S., Serrano-Gotarredona, T., Carrasco, J.-A.P., Linares-Barranco, B., Furber, S.: A Real-Time, Event-Driven Neuromorphic System for Goal-Directed Attentional Selection. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 226–233. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Gamez, D., Fidjeland, A., Lazdins, E.: iSpike: a spiking neural interface for the iCub robot. Bioinspiration and Biomimetics 7(2) (2012)Google Scholar
  7. 7.
    Kuniyoshi, Y., Berthouze, L.: Neural learning of embodied interaction dynamics. Neural Networks 11(7-8), 1259–1276 (1998)CrossRefGoogle Scholar
  8. 8.
    Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F.: The iCub humanoid robot: an open platform for research in embodied cognition. In: Proc. IEEE Workshop on Performance Metrics for Intelligent Systems, PerMIS 2008 (2008)Google Scholar
  9. 9.
    Peniak, M., Morse, A., Cangelosi, A.: Aquila 2.0 software architecture for cognitive robotics. In: Proc. IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL (2013)Google Scholar
  10. 10.
    Rast, A., Partzsch, J., Mayr, C., Schemmel, J., Hartmann, S., Plana, L.A., Temple, S., Lester, D.R., Schüffny, R., Furber, S.: A Location-Independent Direct Link Neuromorphic Interface. In: Proc. 2013 Int’l Joint Conf. Neural Networks (IJCNN 2013), pp. 1967–1974 (2013)Google Scholar
  11. 11.
    Scheier, C., Pfeifer, R., Kuniyoshi, Y.: Embedded neural networks: exploring constraints. Neural Networks 11(7-8), 1551–1569 (1998)CrossRefGoogle Scholar
  12. 12.
    Shmiel, T., Drori, R., Shmiel, O., Ben-Shaul, Y., Nadasdy, Z., Shemesh, M., Teicher, M., Abeles, M.: Temporally precise cortical firing patterns are associated with distinct action segments. The Journal of Neurophysiology 96, 2645–2652 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Samantha V. Adams
    • 1
  • Alexander D. Rast
    • 2
  • Cameron Patterson
    • 1
  • Francesco Galluppi
    • 1
  • Kevin Brohan
    • 1
  • José-Antonio Pérez-Carrasco
    • 1
  • Thomas Wennekers
    • 1
  • Steve Furber
    • 2
  • Angelo Cangelosi
    • 1
  1. 1.Plymouth UniversityPlymouthUK
  2. 2.School of Computer ScienceUniversity of ManchesterManchesterUK

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