neural networks research group
areas
people
projects
demos
publications
software/data
A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer (2016)
David L. Leottau and Javier Ruiz-del-Solar and
Patrick MacAlpine
and
Peter Stone
Hierarchical task decomposition strategies allow robots and agents in general to address complex decision-making tasks. Layered learning is a hierarchical machine learning paradigm where a complex behavior is learned from a series of incrementally trained sub-tasks. This paper describes how layered learning can be applied to design individual behaviors in the context of soccer robotics. Three different layered learning strategies are implemented and analyzed using a ball-dribbling behavior as a case study. Performance indices for evaluating dribbling speed and ball-control are defined and measured. Experimental results validate the usefulness of the implemented layered learning strategies showing a trade-off between performance and learning speed.
View:
PDF
,
HTML
Citation:
In Luis Almeida and Jianmin Ji and Gerald Steinbauer and Sean Luke, editors,
{R}obo{C}up-2015: Robot Soccer World Cup {XIX}
, Berlin, Germany, 2016. Springer Verlag.
Bibtex:
@incollection{LNAI15-Leottau, title={A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer}, author={David L. Leottau and Javier Ruiz-del-Solar and Patrick MacAlpine and Peter Stone}, booktitle={{R}obo{C}up-2015: Robot Soccer World Cup {XIX}}, editor={Luis Almeida and Jianmin Ji and Gerald Steinbauer and Sean Luke}, address={Berlin, Germany}, publisher={Springer Verlag}, url="http://nn.cs.utexas.edu/?leottau:lnai15", year={2016} }
People
Patrick MacAlpine
patmac [at] cs utexas edu
Peter Stone
pstone [at] cs utexas edu
Areas of Interest
Humanoid Robots
Layered Learning
RoboCup
Simulated Robot Soccer