neural networks research group
areas
people
projects
demos
publications
software/data
Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League (2015)
Mike Depinet and
Patrick MacAlpine
and
Peter Stone
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.
View:
PDF
,
PS
,
HTML
Citation:
In H. Levent Akin and Reinaldo A. C. Bianchi and Subramanian Ramamoorthy and Komei Sugiura, editors,
{R}obo{C}up-2014: Robot Soccer World Cup {XVIII}
, 2015. Springer Verlag.
Bibtex:
@incollection{LNAI14-Depinet, title={Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking in the RoboCup 3D Simulation League}, author={Mike Depinet and Patrick MacAlpine and Peter Stone}, 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}, url="http://nn.cs.utexas.edu/?depinet:robocup14", year={2015} }
People
Patrick MacAlpine
patmac [at] cs utexas edu
Peter Stone
pstone [at] cs utexas edu
Areas of Interest
Multi-Robot Systems
Multiagent Systems
RoboCup
Simulated Robot Soccer