| Autor: |
Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Robotics and AI, Vol 9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2296-9144 |
| DOI: |
10.3389/frobt.2022.854212 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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