A unified CPG-based and multi-agent control framework for low-cost quadruped robots

Abstract

Quadruped robots have gained significant attention due to their superior mobility on uneven and unstructured terrains, offering potential applications in inspection, search and rescue, and field exploration. However, achieving robust locomotion on low-cost platforms remains challenging because of constraints in stability, adaptability, sensing quality, and onboard computation. In this work, we present an integrated motion-control framework that combines biologically inspired Central Pattern Generators (CPGs), a multi-agent reinforcement learning coordination layer, and low-cost hardware adaptation to enable reliable and efficient quadruped locomotion. The proposed framework uses CPGs as structured gait priors for rhythmic leg motion, models each leg as a coordinated agent with a shared-parameter residual policy, and incorporates actuator abstraction and safety-aware command projection. The low-cost merit specifically concerns online deployment: the four legs share a single 39,560-parameter actor (approximately 155 KiB in 32-bit precision), evaluated at 50 Hz from compact proprioceptive observations, while the centralized critic, simulation infrastructure, external motion capture, vision-based terrain perception, direct torque sensing, and online dynamics optimization are not required on the robot. We validate the approach in both simulation and on a physical low-cost quadruped robot across obstacles, ramps, stairs, and uneven terrain. Experimental results demonstrate that the integrated system improves locomotion stability, energy efficiency, and terrain adaptability compared with baseline controllers, highlighting the effectiveness of combining a structured gait prior, lightweight residual coordination, and hardware-aware deployment for practical quadruped locomotion.