Coordinating complex motion sequences remains a challenging task for robotics. Machine Learning has aided this process, successfully improving motion sequences such as walking and grasping. However, to the best of our knowledge, outside of simulation, learning has never been applied to the task of kicking the ball. We apply machine learning methods to optimize kick power entirely on a real robot. The resulting learned kick is significantly more powerful than the most powerful hand-coded kick of one of the most successful RoboCup four-legged league teams, and is learned in a principled manner which requires very little engineering of the parameter space. Finally, model inversion is applied to the problem of creating a parameterized kick capable of kicking the ball a specified distance.

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In Robocup International Symposium, 2010.

@inproceedings{hausknecht:robocup10, title={Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker}, author={Matthew Hausknecht and Peter Stone}, booktitle={Robocup International Symposium}, url="http://www.cs.utexas.edu/users/ai-lab/?hausknecht:robocup10", year={2010} }