Abstract: We seek to enable users to teach personal robots arbitrary tasks, allowing robots to better serve users' wants and needs without explicit programming. Robot learning from demonstration is an approach well-suited to this paradigm, as 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 have developed a decision-making learning framework capable of learning both low-level motion primitives (locomotion and manipulation) and high-level tasks built on top of them from interactive demonstration. Thus far, we have applied nonparametric regression within this framework towards learning a complete robot soccer player and successfully taught a robot dog to first walk, and then to seek and acquire a ball.