Robustness in the Long Run: Auto-teaching vs Anticipation in Evolutionary Robotics

  • Nicolas Godzik
  • Marc Schoenauer
  • Michèle Sebag
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)


In Evolutionary Robotics, auto-teaching networks, neural networks that modify their own weights during the life-time of the robot, have been shown to be powerful architectures to develop adaptive controllers. Unfortunately, when run for a longer period of time than that used during evolution, the long-term behavior of such networks can become unpredictable. This paper gives an example of such dangerous behavior, and proposes an alternative solution based on anticipation: as in auto-teaching networks, a secondary network is evolved, but its outputs try to predict the next state of the robot sensors. The weights of the action network are adjusted using some back-propagation procedure based on the errors made by the anticipatory network. First results – in simulated environments – show a tremendous increase in robustness of the long-term behavior of the controller.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Nicolas Godzik
    • 1
  • Marc Schoenauer
    • 1
  • Michèle Sebag
    • 1
  1. 1.TAO teamINRIA Futurs and LRI, UMR CNRS 8623Orsay CedexFrance

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