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.Comment: Submitted to IEEE RA-