Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization.

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.

SUBMITTER: Bogdanovic M 

PROVIDER: S-EPMC9484268 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature