In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a bottom-up design approach that allows users to easily design and experiment with various application scenarios on top of GPU-parallelized simulations. It also offers a range of benchmark tasks, presenting challenges ranging from single-drone hovering to over-actuated system tracking. In summary, we propose an open-sourced drone simulation platform, equipped with an extensive suite of tools for drone learning. It includes 4 drone models, 5 sensor modalities, 4 control modes, over 10 benchmark tasks, and a selection of widely used RL baselines. To showcase the capabilities of OmniDrones and to support future research, we also provide preliminary results on these benchmark tasks. We hope this platform will encourage further studies on applying RL to practical drone systems.

翻译：本文介绍了OmniDrones——一个基于Nvidia Omniverse Isaac Sim构建的、专为无人机控制中的强化学习设计的高效灵活平台。该平台采用自下而上的设计方法，使用户能够在GPU并行化模拟基础上，轻松设计并实验多样化应用场景。平台还提供一系列基准测试任务，涵盖从单机悬停到过驱动系统跟踪等挑战。总而言之，我们提出一个开源无人机模拟平台，配备了一套完整的无人机学习工具集，包括4种无人机模型、5种传感器模态、4种控制模式、10余项基准任务，以及多种主流的强化学习基线算法。为展示OmniDrones的能力并支持后续研究，我们还提供了这些基准任务的初步实验结果。期望本平台能推动强化学习在实用无人机系统中应用的进一步研究。