  Legged locomotion is arguably the most suited and versatile mode to deal with
natural or unstructured terrains. Intensive research into dynamic walking and
running controllers has recently yielded great advances, both in the optimal
control and reinforcement learning (RL) literature. Hopping is a challenging
dynamic task involving a flight phase and has the potential to increase the
traversability of legged robots. Model based control for hopping typically
relies on accurate detection of different jump phases, such as lift-off or
touch down, and using different controllers for each phase. In this paper, we
present a end-to-end RL based torque controller that learns to implicitly
detect the relevant jump phases, removing the need to provide manual heuristics
for state detection. We also extend a method for simulation to reality transfer
of the learned controller to contact rich dynamic tasks, resulting in
successful deployment on the robot after training without parameter tuning.
