论文标题
学习鲁棒和动态机器人运动的低频运动控制
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
论文作者
论文摘要
通常，通过增加运动控制频率来最大程度地提高鲁棒性和反应性的目的是进行机器人运动。我们通过在实际的任何Anymal C上以低至8 Hz的作用来证明强大而动态的运动来挑战这个直观的概念。该机器人能够稳健，重复地达到1.5 m/s的高标题速度，横穿不平坦的地形，并抵抗意外的外部扰动。我们进一步介绍了基于深度加固学习（RL）的运动控制策略的比较分析，该政策以5 Hz至200 Hz的频率训练和执行。我们表明，低频政策对系统动力学的致动潜伏期和变化不太敏感。在某种程度上，即使没有任何动态随机化或致动模型，可以成功执行成功的SIM转移传输。我们通过一系列严格的经验评估来支持这一主张。此外，为了协助可重复性，我们在https://ori-drs.github.io/lfmc/上提供培训和部署代码以及扩展分析。
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/.
