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Mathias Korte

2 accepted papers

2023

Offline Reinforcement Learning for Quadrotor Control: Overcoming the Ground Effect

IROS 2023poster

Applying Reinforcement Learning to solve real-world optimization problems presents significant challenges because of the large amount of data normally required. A popular solution is to train the algorithms in a simulation and transfer the weights to the real system. However, sim-to-real approaches…

Cited by 1SourceScholar
2022

Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors

IROS 2022poster

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 embed-ded hard…

Cited by 12SourcecodeScholar