Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning
Michel Aractingi Pierre-Alexandre Léziart Thomas Flayols Julien Perez Tomi Silander Philippe Souères
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.