Echo State Networks for Mobile Robot Modeling and Control

  • Paul G. Plöger
  • Adriana Arghir
  • Tobias Günther
  • Ramin Hosseiny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3020)


Applications of recurrent neural networks (RNNs) tend to be rare because training is difficult. A recent theoretical breakthrough [Jae01b] called Echo State Networks (ESNs) has made RNN training easy and fast and makes RNNs a versatile tool for many problems. The key idea is training the output weights only of an otherwise topologically unrestricted but contractive network. After outlining the mathematical basics, we apply ESNs to two examples namely to the generation of a dynamical model for a differential drive robot using supervised learning and secondly to the training of a respective motor controller.


Mobile Robot Spectral Radius Extend Kalman Filter Recurrent Neural Network Pulse Width Modulation 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paul G. Plöger
    • 1
  • Adriana Arghir
    • 1
  • Tobias Günther
    • 1
  • Ramin Hosseiny
    • 1
  1. 1.FHG Institute of Autonomous Intelligent SystemsSt AugustinGermany

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