  Robotic locomotion is often approached with the goal of maximizing robustness
and reactivity by increasing motion control frequency. We challenge this
intuitive notion by demonstrating robust and dynamic locomotion with a learned
motion controller executing at as low as 8 Hz on a real ANYmal C quadruped. The
robot is able to robustly and repeatably achieve a high heading velocity of 1.5
m/s, traverse uneven terrain, and resist unexpected external perturbations. We
further present a comparative analysis of deep reinforcement learning (RL)
based motion control policies trained and executed at frequencies ranging from
5 Hz to 200 Hz. We show that low-frequency policies are less sensitive to
actuation latencies and variations in system dynamics. This is to the extent
that a successful sim-to-real transfer can be performed even without any
dynamics randomization or actuation modeling. We support this claim through a
set of rigorous empirical evaluations. Moreover, to assist reproducibility, we
provide the training and deployment code along with an extended analysis at
https://ori-drs.github.io/lfmc/.
