This paper contributes an open-sourced implementation of a neural-network based controller framework within the PX4 stack. We develop a custom module for inference on the microcontroller while retaining all of the functionality of the PX4 autopilot. Policies trained in the Aerial Gym Simulator are converted to the TensorFlow Lite format and then built together with PX4 and flashed to the flight controller. The policies substitute the control-cascade within PX4 to offer an end-to-end position-setpoint tracking controller directly providing normalized motor RPM setpoints. Experiments conducted in simulation and the real-world show similar tracking performance. We thus provide a flight-ready pipeline for testing neural control policies in the real world. The pipeline simplifies the deployment of neural networks on embedded flight controller hardware thereby accelerating research on learning-based control. Both the Aerial Gym Simulator and the PX4 module are open-sourced atthis https URLandthis https URL. Video:this https URL.
View on arXiv@article{hegre2025_2505.00432,
title={ A Neural Network Mode for PX4 on Embedded Flight Controllers },
author={ Sindre M. Hegre and Welf Rehberg and Mihir Kulkarni and Kostas Alexis },
journal={arXiv preprint arXiv:2505.00432},
year={ 2025 }
}