  Replicating the remarkable athleticism seen in animals has long been a
challenge in robotics control. Although Reinforcement Learning (RL) has
demonstrated significant progress in dynamic legged locomotion control, the
substantial sim-to-real gap often hinders the real-world demonstration of truly
dynamic movements. We propose a new framework to mitigate this gap through
frequency-domain analysis-based impedance matching between simulated and real
robots. Our framework offers a structured guideline for parameter selection and
the range for dynamics randomization in simulation, thus facilitating a safe
sim-to-real transfer. The learned policy using our framework enabled jumps
across distances of 55 cm and heights of 38 cm. The results are, to the best of
our knowledge, one of the highest and longest running jumps demonstrated by an
RL-based control policy in a real quadruped robot. Note that the achieved
jumping height is approximately 85% of that obtained from a state-of-the-art
trajectory optimization method, which can be seen as the physical limit for the
given robot hardware. In addition, our control policy accomplished stable
walking at speeds up to 2 m/s in the forward and backward directions, and 1 m/s
in the sideway direction.
