Learning robot soccer skills from demonstration

Abstract

We seek to enable users to teach personal robots arbitrary tasks so that the robot can better perform as the user desires without explicit programming. Robot learning from demonstration is an approach well-suited to this paradigm, as a robot learns new tasks from observations of the task itself. Many current robot learning algorithms require the existence of basic behaviors that can be combined to perform the desired task. However, robots that exist in the world for long timeframes and learn many tasks over their lifetime may exhaust this basis set and need to move beyond it. In particular, we are interested in a robot that must learn to perform an unknown task for which its built in behaviors may not be appropriate. We demonstrate a learning paradigm that is capable of learning both low-level motion primitives (locomotion and manipulation) and high-level tasks built on top of them from interactive demonstration. We apply nonparametric regression within this framework towards learning a complete robot soccer player and successfully teach a robot dog to first walk, and then to seek and acquire a ball.