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

• URL: http://arxiv.org/abs/2209.14887v1
• Date: Thu, 29 Sep 2022 15:55:33 GMT
• Title: Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
• Authors: Siddhant Gangapurwala, Luigi Campanaro and Ioannis Havoutis
• Abstract summary: We demonstrate 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.
• Score: 10.838285018473725
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 https://ori-drs.github.io/lfmc/.

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