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
this https URL.

通过在真实机器人 ANYmal C 上执行 8-200Hz 的深度强化学习基于动作控制策略，我们展示了使用低频控制某种程度上可以实现比高频控制更加鲁棒和动态的机器人运动，这在不考虑动力学随机化或作用建模的情况下即可成功进行模拟到实物的转移。