Adaptive Energy Regularization for Autonomous Gait Transition and Energy-Efficient Quadruped Locomotion

Boyuan Liang, Lingfeng Sun, Xinghao Zhu, Bike Zhang, Ziyin Xiong, Yixiao Wang, Chenran Li, Koushil Sreenath, Masayoshi Tomizuka

Keywords: Quadrupedal Locomotion, Reinforcement Learning, Energy-efficient Locomotion

TL;DR: This paper presents a novel approach to energy-efficient locomotion in quadruped robots by implementing a simplified, energy-centric reward strategy within a reinforcement learning framework.

Abstract: We investigate the impact of incorporating an energy-efficient reward term that prioritizes distance-averaged energy consumption into the reinforcement learning framework. Our findings demonstrate that this simple addition enables quadruped robots to autonomously select appropriate gaits—such as four-beat walking at lower speeds and trotting at higher speeds—without the need for explicit gait regularizations. Furthermore, we provide a guideline for tuning the weight of this energy-efficient reward, facilitating its application in real-world scenarios. The effectiveness of our approach is validated through simulations and on a real Unitree Go1 robot. This research highlights the potential of energy-centric reward functions to simplify and enhance the learning of adaptive and efficient locomotion in quadruped robots.

Submission Number: 11