Learning Robot Soccer from Demonstration: Ball Grasping

We approach floor-level robotic manipulation from a Learning from Demonstration direction. In this paradigm, a robot learns new tasks in new environments 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 may exhaust this basis set. In particular, a robot may be asked to perform an unknown task for which its built in behaviors may not be appropriate. We demonstrate a learning framework that is capable of learning both low-level motion primitives 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 grasp a ball.