AUTHOR=Bogdanovic Miroslav , Khadiv  Majid , Righetti  Ludovic TITLE=Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization JOURNAL=Frontiers in Robotics and AI VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2022.854212 DOI=10.3389/frobt.2022.854212 ISSN=2296-9144 ABSTRACT=

We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization. The demonstration is used in the first stage as a starting point to facilitate initial exploration. In the second stage, the relevant task reward is optimized directly and a policy robust to environment uncertainties is computed. We demonstrate and examine in detail the performance and robustness of our approach on highly dynamic hopping and bounding tasks on a quadruped robot.