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.
@article{arxiv.2201.01369,
title = {Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors},
author = {Sven Gronauer and Matthias Kissel and Luca Sacchetto and Mathias Korte and Klaus Diepold},
journal= {arXiv preprint arXiv:2201.01369},
year = {2022}
}