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 is seamlessly wrapped with the Gym environment, facilitating the straightforward implementation of various learning algorithms. It supports the directly importing open-source scene datasets compatible with Habitat-Sim, enabling training on diverse real-world environments simultaneously. To validate our simulator, we also make three reinforcement learning examples for typical flight tasks relying on visual observations. The simulator is now available at [https://github.com/SJTU-ViSYS-team/VisFly].

翻译：本文提出VisFly，一种专为利用强化学习算法高效训练基于视觉的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每秒超过10,000帧的帧率。该模拟器集成了可微分物理引擎，并与Gym环境无缝对接，便于各类学习算法的直接实现。它支持直接导入与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并行训练。为验证本模拟器的有效性，我们还针对依赖视觉观测的典型飞行任务提供了三个强化学习示例。该模拟器现已发布于[https://github.com/SJTU-ViSYS-team/VisFly]。