Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization

Miroslav Bogdanovic; Majid Khadiv; Ludovic Righetti

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arXiv · 2107.06629

Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization

Miroslav Bogdanovic·Majid Khadiv·Ludovic Righetti

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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.

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Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti. 2021-07-14. Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization. https://arxiv.org/abs/2107.06629

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