Peter Stone's Selected Publications

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Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League

Mike Depinet, Patrick MacAlpine, and Peter Stone. Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League. In H. Levent Akin, Reinaldo A. C. Bianchi, Subramanian Ramamoorthy, and Komei Sugiura, editors, RoboCup-2014: Robot Soccer World Cup XVIII, Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin, 2015.
Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2014/html/learningFromObservation.html

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Abstract

Even with improvements in machine learning enabling robots to quickly optimize and perfect their skills, developing a seed skill from which to begin an optimization remains a necessary challenge for large action spaces. This paper proposes a method for creating and using such a seed by i) observing the effects of the actions of another robot, ii) further optimizing the skill starting from this seed, and iii) embedding the optimized skill in a full behavior. Called KSOBI, this method is fully implemented and tested in the complex RoboCup 3D simulation domain. To the best of our knowledge, the resulting skill kicks the ball farther in this simulator than has been previously documented.

BibTeX Entry

@incollection{LNAI14-Depinet,
  author = {Mike Depinet and Patrick MacAlpine and Peter Stone},
  title = {Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League},
  booktitle = {{R}obo{C}up-2014: Robot Soccer World Cup {XVIII}},
  Editor={H. Levent Akin and Reinaldo A. C. Bianchi and Subramanian Ramamoorthy and Komei Sugiura},
  Publisher="Springer Verlag",
  address="Berlin",
  year="2015",
  series="Lecture Notes in Artificial Intelligence",
  abstract={
    Even with improvements in machine learning enabling robots to quickly 
optimize and perfect their skills, developing a seed skill from which to begin 
an optimization remains a necessary challenge for large action spaces.  This 
paper proposes a method for creating and using such a seed by i) observing the 
effects of the actions of another robot, ii) further optimizing the skill 
starting from this seed, and iii) embedding the optimized skill in a full 
behavior.  Called KSOBI, this method is fully implemented and tested in the 
complex RoboCup 3D simulation domain.  To the best of our knowledge, the 
resulting skill kicks the ball farther in this simulator than has been 
previously documented.
  },
  wwwnote={Accompanying videos at <a href="http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2014/html/learningFromObservation.html">http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2014/html/learningFromObservation.html</a>},
} 

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