Energy-Efficient Quadruped Locomotion with Compliant Feet

TL;DR

This study demonstrates that incorporating optimally tuned compliant feet into quadruped robots significantly reduces energy consumption during walking, as shown through simulation and real-world experiments.

Contribution

The paper introduces a reinforcement learning approach to optimize foot compliance in quadruped robots, revealing energy efficiency benefits of compliant feet.

Findings

Energy consumption reduced by ~17% with optimal spring stiffness.

Simulation and real-world results show benefits of compliant feet.

Optimal foot compliance improves efficiency without destabilizing the robot.

Abstract

Quadruped robots are often designed with rigid feet to simplify control and maintain stable contact during locomotion. While this approach is straightforward, it limits the ability of the legs to absorb impact forces and reuse stored elastic energy, leading to higher energy expenditure during locomotion. To explore whether compliant feet can provide an advantage, we integrate foot compliance into a reinforcement learning (RL) locomotion controller and study its effect on walking efficiency. In simulation, we train eight policies corresponding to eight different spring stiffness values and then cross-evaluate their performance by measuring mechanical energy consumed per meter traveled. In experiments done on a developed quadruped, the energy consumption for the intermediate stiffness spring is lower by ~ 17% when compared to a very stiff or a very flexible spring incorporated in the feet, with similar trends appearing in the simulation results. These results indicate that selecting an appropriate foot compliance can improve locomotion efficiency without destabilizing the robot during motion.