# Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion

arXiv: https://arxiv.org/abs/2212.14400 · HTML: https://mlsanity.com/paper/2212.14400/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 27 of 27)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [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)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (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)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers](https://mlsanity.com/paper/2107.03996/) (2021)
- [RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control](https://mlsanity.com/paper/2012.03094/) (2020)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.08399/) (2019)
- [Policies Modulating Trajectory Generators](https://mlsanity.com/paper/1910.02812/) (2019)
- [Learning to Jump from Pixels](https://mlsanity.com/paper/2110.15344/) (2021)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [Robust Quadruped Jumping via Deep Reinforcement Learning](https://mlsanity.com/paper/2011.07089/) (2020)
- [Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization](https://mlsanity.com/paper/2109.14549/) (2021)
- [Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning](https://mlsanity.com/paper/2103.06484/) (2021)
- [TAMOLS: Terrain-Aware Motion Optimization for Legged Systems](https://mlsanity.com/paper/2206.14049/) (2022)
- [Vision-aided Dynamic Quadrupedal Locomotion on Discrete Terrain using Motion Libraries](https://mlsanity.com/paper/2110.00891/) (2021)
- [PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations](https://mlsanity.com/paper/2207.13224/) (2022)
- [Training in Task Space to Speed Up and Guide Reinforcement Learning](https://mlsanity.com/paper/1903.02219/) (2019)
- [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, 14 of 14)

- [ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots](https://mlsanity.com/paper/2310.10486/) (2023)
- [QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots](https://mlsanity.com/paper/2508.02512/) (2025)
- [Time-Efficient Reinforcement Learning with Stochastic Stateful Policies](https://mlsanity.com/paper/2311.04082/) (2023)
- [Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds](https://mlsanity.com/paper/2607.18135/) (2026)
- [Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2602.06967/) (2025)
- [TRANS: Terrain-aware Reinforcement Learning for Agile Navigation of Quadruped Robots under Social Interactions](https://mlsanity.com/paper/2602.12724/) (2026)
- [A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2311.07822/) (2023)
- [Hierarchical learning control for autonomous robots inspired by central nervous system](https://mlsanity.com/paper/2408.03525/) (2024)
- [PALo: Learning Posture-Aware Locomotion for Quadruped Robots](https://mlsanity.com/paper/2503.04462/) (2025)
- [PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion](https://mlsanity.com/paper/2510.18348/) (2025)
- [Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots](https://mlsanity.com/paper/2604.12909/) (2026)
- [Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization](https://mlsanity.com/paper/2604.20365/) (2026)
- [Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain](https://mlsanity.com/paper/2605.09595/) (2026)
- [ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders](https://mlsanity.com/paper/2605.19503/) (2026)

## Abstract (arXiv)

We present a framework for learning visually-guided quadruped locomotion by integrating exteroceptive sensing and central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework. Through both exteroceptive and proprioceptive sensing, the agent learns to coordinate rhythmic behavior among different oscillators to track velocity commands, while at the same time override these commands to avoid collisions with the environment. We investigate several open robotics and neuroscience questions: 1) What is the role of explicit interoscillator couplings between oscillators, and can such coupling improve sim-to-real transfer for navigation robustness? 2) What are the effects of using a memory-enabled vs. a memory-free policy network with respect to robustness, energy-efficiency, and tracking performance in sim-to-real navigation tasks? 3) How do animals manage to tolerate high sensorimotor delays, yet still produce smooth and robust gaits? To answer these questions, we train our perceptive locomotion policies in simulation and perform sim-to-real transfers to the Unitree Go1 quadruped, where we observe robust navigation in a variety of scenarios. Our results show that the CPG, explicit interoscillator couplings, and memory-enabled policy representations are all beneficial for energy efficiency, robustness to noise and sensory delays of 90 ms, and tracking performance for successful sim-to-real transfer for navigation tasks. Video results can be found at https://youtu.be/wpsbSMzIwgM.