Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

Abstract: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.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2209.14887 [cs.RO]

(or arXiv:2209.14887v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2209.14887 Focus to learn more arXiv-issued DOI via DataCite

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