• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League.
Mike Depinet, Patrick MacAlpine,
and Peter Stone.
In Reinaldo A. C. Bianchi, H. Levent Akin, 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
[PDF]905.3kB [postscript]42.3MB
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.
@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={Reinaldo A. C. Bianchi and H. Levent Akin 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>},
}
Generated by bib2html.pl (written by Patrick Riley ) on Sun Apr 14, 2024 10:20:44