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

arXiv: https://arxiv.org/abs/2209.14887 · HTML: https://mlsanity.com/paper/2209.14887/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2105.08328/) (2021)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [Multi-expert learning of adaptive legged locomotion](https://mlsanity.com/paper/2012.05810/) (2020)
- [Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion](https://mlsanity.com/paper/2011.02404/) (2020)
- [RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control](https://mlsanity.com/paper/2012.03094/) (2020)
- [Fast and Efficient Locomotion via Learned Gait Transitions](https://mlsanity.com/paper/2104.04644/) (2021)
- [Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?](https://mlsanity.com/paper/1611.01055/) (2016)
- [Feedback Control For Cassie With Deep Reinforcement Learning](https://mlsanity.com/paper/1803.05580/) (2018)
- [Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search](https://mlsanity.com/paper/1509.06791/) (2015)
- [Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion](https://mlsanity.com/paper/2002.09676/) (2020)
- [Feedback MPC for Torque-Controlled Legged Robots](https://mlsanity.com/paper/1905.06144/) (2019)
- [Learning Task Space Actions for Bipedal Locomotion](https://mlsanity.com/paper/2011.04741/) (2020)
- [Hybrid leg compliance enables robots to operate with sensorimotor delays and low control update frequencies](https://mlsanity.com/paper/2101.12255/) (2021)

## Built on by (most cited first, 12 of 12)

- [Prompt a Robot to Walk with Large Language Models](https://mlsanity.com/paper/2309.09969/) (2023)
- [Tune to Learn: How Controller Gains Shape Robot Policy Learning](https://mlsanity.com/paper/2604.02523/) (2026)
- [Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2410.07877/) (2024)
- [Offline Adaptation of Quadruped Locomotion using Diffusion Models](https://mlsanity.com/paper/2411.08832/) (2024)
- [Efficient Reinforcement Learning for Jumping Monopods](https://mlsanity.com/paper/2309.07038/) (2023)
- [TARC: Time-Adaptive Robotic Control](https://mlsanity.com/paper/2510.23176/) (2025)
- [BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO](https://mlsanity.com/paper/2406.17490/) (2024)
- [Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour](https://mlsanity.com/paper/2509.02727/) (2025)
- [GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots](https://mlsanity.com/paper/2509.17582/) (2025)
- [PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion](https://mlsanity.com/paper/2510.18348/) (2025)
- [Learning Perceptive Platform Adaptive Locomotion Controllers for Quadrupedal Robots](https://mlsanity.com/paper/2606.25179/) (2026)
- [Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC](https://mlsanity.com/paper/2609.32591/) (2026)

## Abstract (arXiv)

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://articulated.robots.ox.ac.uk/lfmc/.