# SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion

arXiv: https://arxiv.org/abs/2310.06606 · HTML: https://mlsanity.com/paper/2310.06606/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots](https://mlsanity.com/paper/2407.04224/) (2024)
- [Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives](https://mlsanity.com/paper/2410.10438/) (2024)
- [Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds](https://mlsanity.com/paper/2607.18135/) (2026)
- [SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning](https://mlsanity.com/paper/2502.12674/) (2025)
- [Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots](https://mlsanity.com/paper/2604.12909/) (2026)
- [ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders](https://mlsanity.com/paper/2605.19503/) (2026)

## Abstract (arXiv)

The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often compromises the system's control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties, has gained significant traction in robotics due to its inherent adaptability and robustness. However, initiating traditional RL approaches from the ground up presents computational challenges and a heightened risk of converging to suboptimal local minima. In this paper, we propose an innovative quadruped locomotion framework, SYNLOCO, by synthesizing CPG and RL that can ingeniously integrate the strengths of both methods, enabling the development of a locomotion controller that is both stable and natural. Furthermore, we introduce a set of performance-driven reward metrics that augment the learning of locomotion control. To optimize the learning trajectory of SYNLOCO, a two-phased training strategy is presented. Our empirical evaluation, conducted on a Unitree GO1 robot under varied conditions--including distinct velocities, terrains, and payload capacities--showcases SYNLOCO's ability to produce consistent and clear-footed gaits across diverse scenarios. The developed controller exhibits resilience against substantial parameter variations, underscoring its potential for robust real-world applications.