Graceful Degradation Under Noise on Brain Inspired Robot Controllers

  • Ricardo de Azambuja
  • Frederico B. Klein
  • Martin F. Stoelen
  • Samantha V. Adams
  • Angelo Cangelosi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9947)

Abstract

How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker.

Keywords

SNN Liquid state machines Robot control Noise Graceful degradation Robustness 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ricardo de Azambuja
    • 1
    • 2
  • Frederico B. Klein
    • 1
  • Martin F. Stoelen
    • 1
  • Samantha V. Adams
    • 1
  • Angelo Cangelosi
    • 1
  1. 1.School of Computing, Electronics and MathematicsPlymouth UniversityPlymouthUK
  2. 2.CAPES FoundationMinistry of Education of BrazilBrasiliaBrazil

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