Author:
Aractingi Michel,Léziart Pierre-Alexandre,Flayols Thomas,Perez Julien,Silander Tomi,Souères Philippe
Abstract
AbstractQuadruped robots require robust and general locomotion skills to exploit their mobility potential in complex and challenging environments. In this work, we present an 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 and easy to deploy. We detail the learning procedure and method for transfer on the real robot. We show elaborate experiments. Finally, we present experimental results of the learned locomotion on various grounds indoors and outdoors. These results 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.
Funder
Work done as part of the PHD thesis of Michel Aractingi that is funded by Naverlabs Europe and the ANRT of France
Publisher
Springer Science and Business Media LLC
Cited by
3 articles.
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