Self-exploration of the Stumpy Robot with Predictive Information Maximization

  • Georg Martius
  • Luisa Jahn
  • Helmut Hauser
  • Verena V. Hafner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8575)

Abstract

One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.

Keywords

Self-exploration intrinsic motivation robot control information theory dynamical systems learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Georg Martius
    • 1
  • Luisa Jahn
    • 2
    • 3
  • Helmut Hauser
    • 3
  • Verena V. Hafner
    • 2
  1. 1.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  2. 2.Institut für InformatikHumboldt-Universität zu BerlinBerlinGermany
  3. 3.Artificial Intelligence LabUniversity of ZurichZurichSwitzerland

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