Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Chin Pinch: A Case Study in Skill Learning on a Legged Robot
RoboCup 2006: Robot Soccer World Cup X
Autonomous Learning of Ball Trapping in the Four-Legged Robot League
RoboCup 2006: Robot Soccer World Cup X
Autonomous Learning of Stable Quadruped Locomotion
RoboCup 2006: Robot Soccer World Cup X
Instance-Based Action Models for Fast Action Planning
RoboCup 2007: Robot Soccer World Cup XI
Layered Learning for a Soccer Legged Robot Helped with a 3D Simulator
RoboCup 2007: Robot Soccer World Cup XI
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
Learning to kick the ball using back to reality
RoboCup 2004
Perceiving forces, bumps, and touches from proprioceptive expectations
Robot Soccer World Cup XV
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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 handcoded 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.