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Freek Stulp, Alexandra Kirsch, Suat Gedikli, and Michael Beetz. AGILO RoboCuppers 2004. In RoboCup International Symposium 2004, July 2004.

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Abstract

The Agilo RoboCup team is the primary platform for our research on the semi-automatic acquisition of visuo-motoric plans. It is realized using inexpensive, off the shelf, easily extendible hardware components and a standard software environment. The control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation. The action selection module selects actions according to specified goals as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays.

BibTeX Entry

@InProceedings{stulp04agilo,
  author =            {Freek Stulp and Alexandra Kirsch and Suat Gedikli and Michael Beetz},
  title =             {{AGILO RoboCuppers} 2004},
  booktitle =         {RoboCup International Symposium 2004},
  series =            {Lisbon},
  year =              {2004},
  month =             {July},
  pdf =               {http://www9.in.tum.de/publications/2004/ROBOCUP-SYMP-04-AGILO-TDP-Stulp-etal.pdf},
  url =               {http://citeseer.ist.psu.edu/695117.html},
  bib2html_pubtype =  {Team Description Paper},
  bib2html_rescat =   {Robotics - RoboCup},
  abstract =          {The Agilo RoboCup team is the primary platform for our
  research on the semi-automatic acquisition of visuo-motoric plans. It is
  realized using inexpensive, off the shelf, easily extendible hardware
  components and a standard software environment. The control system of an
  autonomous soccer robot consists of a probabilistic game state estimator and a
  situated action selection module. The game state estimator computes the
  robot's belief state with respect to the current game situation. The action
  selection module selects actions according to specified goals as well as
  learned experiences. Automatic learning techniques made it possible to develop
  fast and skillful routines for approaching the ball, assigning roles, and
  performing coordinated plays.}
}

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