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Entropy-Based Active Vision for a Humanoid Soccer Robot

  • Andreas Seekircher
  • Tim Laue
  • Thomas Röfer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6556)

Abstract

In this paper, we show how the estimation of a robot’s world model can be improved by actively sensing the environment through considering the current world state estimate through minimizing the entropy of an underlying particle distribution. Being originally computationally expensive, this approach is optimized to become executable in real-time on a robot with limited resources. We demonstrate the approach on a humanoid robot, performing self-localization and ball tracking on a RoboCup soccer field.

Keywords

Mutual Information Information Gain Humanoid Robot Active Vision Goal Post 
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 2011

Authors and Affiliations

  • Andreas Seekircher
    • 1
  • Tim Laue
    • 2
  • Thomas Röfer
    • 2
  1. 1.Department of Computer ScienceUniversity of MiamiCoral GablesUSA
  2. 2.Deutsches Forschungszentrum für Künstliche IntelligenzSichere Kognitive SystemeBremenGermany

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