  We present a general, two-stage reinforcement learning approach to create
robust policies that can be deployed on real robots without any additional
training using a single demonstration generated by trajectory optimization. The
demonstration is used in the first stage as a starting point to facilitate
initial exploration. In the second stage, the relevant task reward is optimized
directly and a policy robust to environment uncertainties is computed. We
demonstrate and examine in detail the performance and robustness of our
approach on highly dynamic hopping and bounding tasks on a quadruped robot.
