为无人机学习控制提供全流程框架，实现复杂任务的可靠飞行与真实环境部署。
Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
- 构建端到端强化学习的完整软硬件框架，支持从仿真到实机的闭环训练。
- 在真实干扰下实现稳定飞行和良好的仿真到现实迁移能力。
- 开源基础设施降低学习型控制器部署门槛，适合科研与工程团队使用。
将机器人学习方法应用于非结构化环境中的空中机器人仍具挑战性且前景广阔。尽管深度强化学习（DRL）已实现端到端飞行控制，但该领域仍缺乏系统的设计准则和统一的基础设施，难以实现可复现的训练与真实部署。本文提出一种面向任务的四旋翼端到端DRL框架，整合复杂任务定义的设计原则，揭示仿真任务设定、训练设计与物理部署之间的相互依赖关系。框架涵盖软件基础设施、硬件平台及开源固件，支持全栈式学习流程。大量实验证明，在真实环境扰动下仍具备稳健飞行能力和良好的仿真到现实泛化性能。通过降低基于学习的控制器在空中机器人上的部署门槛，本工作为动态、非结构化环境中自主飞行的发展奠定了实用基础。
原文摘要 · Abstract (English)
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.
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