We present VisFly, a quadrotor simulator designed to efficiently train vision-based flight policies using reinforcement learning algorithms. VisFly offers a user-friendly framework and interfaces, leveraging Habitat-Sim's rendering engines to achieve frame rates exceeding 10,000 frames per second for rendering motion and sensor data. The simulator incorporates differentiable physics and seamlessly integrates with the Gym environment, facilitating the straightforward implementation of various learning algorithms. It supports the direct import of all open-source scene datasets compatible with Habitat-Sim, enabling training on diverse real-world environments and ensuring fair comparisons of learned flight policies. We also propose a general policy architecture for three typical flight tasks relying on visual observations, which have been validated in our simulator using reinforcement learning. The simulator will be available at [https://github.com/SJTU-ViSYS/VisFly].

翻译：本文提出VisFly，一种专为利用强化学习算法高效训练视觉飞行策略而设计的四旋翼飞行器模拟器。VisFly提供用户友好的框架与接口，借助Habitat-Sim的渲染引擎，在渲染运动与传感器数据时实现超过每秒10,000帧的帧率。该模拟器集成了可微分物理引擎，并与Gym环境无缝衔接，便于各类学习算法的直接部署。它支持直接导入所有与Habitat-Sim兼容的开源场景数据集，从而能够在多样化的真实世界环境中进行训练，并确保所学飞行策略的公平比较。我们还针对三种典型的依赖视觉观测的飞行任务提出了一种通用策略架构，该架构已通过强化学习在我们的模拟器中得到验证。本模拟器将在[https://github.com/SJTU-ViSYS/VisFly]开源发布。