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.

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In {R}obo{C}up-2014: Robot Soccer World Cup {XVIII}, H. Levent Akin and Reinaldo A. C. Bianchi and Subramanian Ramamoorthy and Komei Sugiura (Eds.) 2015. Springer Verlag.

@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://www.cs.utexas.edu/users/ai-lab?depinet:robocup14", year={2015} }