Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning

TL;DR

This paper presents a novel deep reinforcement learning approach to develop a robust, energy-efficient, and easily deployable controller for the Solo12 quadruped robot, enabling complex locomotion in challenging environments.

Contribution

First implementation of an end-to-end learning-based controller on Solo12 using deep reinforcement learning of joint impedance references.

Findings

Controller follows velocity commands accurately

Energy consumption is optimized

Easy transfer and deployment on real robot

Abstract

Quadruped robots require robust and general locomotion skills to exploit their mobility potential in complex and challenging environments. In this work, we present the first implementation of a robust end-to-end learning-based controller on the Solo12 quadruped. Our method is based on deep reinforcement learning of joint impedance references. The resulting control policies follow a commanded velocity reference while being efficient in its energy consumption, robust and easy to deploy. We detail the learning procedure and method for transfer on the real robot. In our experiments, we show that the Solo12 robot is a suitable open-source platform for research combining learning and control because of the easiness in transferring and deploying learned controllers.