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

• URL: http://arxiv.org/abs/2201.01369v1
• Date: Tue, 4 Jan 2022 22:32:05 GMT
• Title: Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors
• Authors: Sven Gronauer, Matthias Kissel, Luca Sacchetto, Mathias Korte, Klaus Diepold
• Abstract summary: We show that it is possible to train low-level control policies with reinforcement learning entirely in simulation and deploy them on a quadrotor robot without using real-world data to fine-tune. Our neural network-based policies use only onboard sensor data and run entirely on the embedded drone hardware.
• Score: 0.14999444543328289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we show that it is possible to train low-level control policies with reinforcement learning entirely in simulation and, then, deploy them on a quadrotor robot without using real-world data to fine-tune. To render zero-shot policy transfers feasible, we apply simulation optimization to narrow the reality gap. Our neural network-based policies use only onboard sensor data and run entirely on the embedded drone hardware. In extensive real-world experiments, we compare three different control structures ranging from low-level pulse-width-modulated motor commands to high-level attitude control based on nested proportional-integral-derivative controllers. Our experiments show that low-level controllers trained with reinforcement learning require a more accurate simulation than higher-level control policies.

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