  Robots that can learn in the physical world will be important to en-able
robots to escape their stiff and pre-programmed movements. For dynamic
high-acceleration tasks, such as juggling, learning in the real-world is
particularly challenging as one must push the limits of the robot and its
actuation without harming the system, amplifying the necessity of sample
efficiency and safety for robot learning algorithms. In contrast to prior work
which mainly focuses on the learning algorithm, we propose a learning system,
that directly incorporates these requirements in the design of the policy
representation, initialization, and optimization. We demonstrate that this
system enables the high-speed Barrett WAM manipulator to learn juggling two
balls from 56 minutes of experience with a binary reward signal. The final
policy juggles continuously for up to 33 minutes or about 4500 repeated
catches. The videos documenting the learning process and the evaluation can be
found at https://sites.google.com/view/jugglingbot
