| Autor: |
Gronauer, Sven, Kissel, Matthias, Sacchetto, Luca, Korte, Mathias, Diepold, Klaus |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/IROS47612.2022.9981229 |
| Popis: |
In this work, we propose a data-driven approach to optimize the parameters of a simulation such that control policies can be directly transferred from simulation to a real-world quadrotor. Our neural network-based policies take only onboard sensor data as input and run entirely on the embedded hardware. In extensive real-world experiments, we compare low-level Pulse-Width Modulated control with higher-level control structures such as Attitude Rate and Attitude, which utilize Proportional-Integral-Derivative controllers to output motor commands. Our experiments show that low-level controllers trained with reinforcement learning require a more accurate simulation than higher-level control policies. |
| Databáze: |
arXiv |
| Externí odkaz: |
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