Towards a Neural Hierarchy of Time Scales for Motor Control

  • Tim Waegeman
  • Francis Wyffels
  • Benjamin Schrauwen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)


Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control.


Locomotion Control Hierarchy Adaptive control Feedback control Central Pattern Generator Reservoir computing (RC) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tim Waegeman
    • 1
  • Francis Wyffels
    • 1
  • Benjamin Schrauwen
    • 1
  1. 1.Department of Electronics and Information SystemsGhent UniversityGhentBelgium

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