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/.

翻译：机器人运动通常以通过提高运动控制频率来最大化鲁棒性和反应性为目标。我们挑战这一直观认知，通过在实际 ANYmal C 四足机器人上以低至 8 Hz 频率执行学习的运动控制器，展示了鲁棒且动态的运动能力。该机器人能够稳健且可重复地达到 1.5 m/s 的高前进速度，穿越不平坦地形，并抵抗意外外部扰动。我们进一步对基于深度强化学习（RL）的运动控制策略进行了比较分析，这些策略在 5 Hz 至 200 Hz 的频率范围内进行训练和执行。研究表明，低频策略对执行延迟和系统动力学变化较不敏感，以至于即使没有任何动力学随机化或执行建模，也能成功实现仿真到实物的迁移。我们通过一系列严格的实证评估支持了这一论断。此外，为促进可重复性，我们在 https://ori-drs.github.io/lfmc/ 提供了训练和部署代码及扩展分析。