Deploying robot learning methods to aerial robots in unstructured environments remains both challenging and promising. While recent advances in deep reinforcement learning (DRL) have enabled end-to-end flight control, the field still lacks systematic design guidelines and a unified infrastructure to support reproducible training and real-world deployment. We present a task-oriented framework for end-to-end DRL in quadrotors that integrates design principles for complex task specification and reveals the interdependencies among simulated task definition, training design principles, and physical deployment. Our framework involves software infrastructure, hardware platforms, and open-source firmware to support a full-stack learning infrastructure and workflow. Extensive empirical results demonstrate robust flight and sim-to-real generalization under real-world disturbances. By reducing the entry barrier for deploying learning-based controllers on aerial robots, our work lays a practical foundation for advancing autonomous flight in dynamic and unstructured environments.
@article{arxiv.2504.15129,
title = {Towards Task-Oriented Flying: Framework, Infrastructure, and Principles},
author = {Kangyao Huang and Hao Wang and Jingyu Chen and Jintao Chen and Yu Luo and Di Guo and Xiangkui Zhang and Xiangyang Ji and Huaping Liu},
journal= {arXiv preprint arXiv:2504.15129},
year = {2025}
}