Learning-based Co-design for Bio-inspired Quadrupeds

Abstract

Designing robotic systems such as quadrupeds is challenging due to the intricate relationship between motion and design, particularly when aiming to replicate the agility, efficiency, and versatility of animals. Co-design simplifies robotic development by simultaneously optimizing physical design and control algorithms in an integrated way. While most prior work validates co-design approaches in simulation, our research bridges this gap by transitioning optimized designs to real-world implementation. To achieve this, we developed a modular quadruped platform with bio-inspired legs that enables the physical implementation of the optimized designs. Our design space, which includes leg segment lengths, spring stiffness, and engagement angle, was optimized to maximize energy efficiency for real-world tasks. We propose a simplified learning-based co-design framework that combines reinforcement learning to create a universal locomotion controller with Bayesian optimization to select the best design. Real-world tests demonstrate a significant reduction in the cost of transport—18.6% for inspection tasks and 35.7% for payload tasks—compared to the nominal design without springs. In simulations, the universal controller adapts well across robot configurations, and the optimization process remains consistent across runs. Although some discrepancies between simulation and real-world performance remain, our findings underscore the potential of co-design to address complex trade-offs in real-world robotic system design.