面向任务的飞行：框架、基础设施与原则
Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
AI总结：
本文提出面向任务的四旋翼端到端DRL框架，整合复杂任务规范的设计原则，揭示模拟任务定义、训练设计原则和物理部署间的相互依赖关系，为动态无结构环境下的自主飞行提供实用基础。
摘要：
将机器人学习方法应用于无结构环境中的空中机器人仍然充满挑战性和前景。尽管最近深度强化学习（DRL）的进展使得端到端飞行控制成为可能，但该领域仍缺乏系统的设计指南和统一的基础设施来支持可重复的训练和实际应用。我们提出了一种面向任务的四旋翼端到端DRL框架，该框架整合了复杂任务规范的设计原则，并揭示了模拟任务定义、训练设计原则和物理部署之间的相互依赖关系。我们的框架包括软件基础设施、硬件平台和开源固件，以支持完整的学习基础设施和工作流程。广泛的实证结果表明，在现实世界干扰下，飞行表现稳健且模拟到现实的泛化能力良好。通过降低在空中机器人上部署基于学习的控制器的门槛，我们的工作为在动态和无结构环境中推进自主飞行奠定了实用基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
