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@incollection{LNAI10-hausknecht,
  author = "Matthew Hausknecht and Peter Stone",
  title = "Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker",
  booktitle= "{R}obo{C}up-2010: Robot Soccer World Cup {XIV}",
  Editor={Javier Ruiz-del-Solar and Eric Chown and Paul G. Pl\"oger},
  Publisher="Springer Verlag",
  address="Berlin",
  year="2011",
  series="Lecture Notes in Artificial Intelligence",
  volume="6556",
  pages="254--65",
  abstract = { 
    Coordinating complex motion sequences remains a challenging task for 
    robotics. Machine Learning has aided this process, successfully
    improving motion sequences such as walking and grasping. However, to
    the best of our knowledge, outside of simulation, learning has never
    been applied to the task of kicking the ball. We apply machine
    learning methods to optimize kick power entirely on a real robot. The 
    resulting learned kick is significantly more powerful than the most
    powerful hand-coded kick of one of the most successful RoboCup
    four-legged league teams, and is learned in a principled manner which
    requires very little engineering of the parameter space. Finally,
    model inversion is applied to the problem of creating a parameterized
    kick capable of kicking the ball a specified distance.
  },  
  wwwnote={Video and source code available at <a href="http://www.cs.utexas.edu/~AustinVilla/?p=research/aibo_kick">http://www.cs.utexas.edu/~AustinVilla/?p=research/aibo_kick</a>},
}