OmniDrones : An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
IEEE Robotics and Automation Letters（2024）
摘要
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.
更多查看译文
关键词
Reinforcement Learning,Simulation and Animation,Machine Learning for Robot Control,Software Tools for Benchmarking and Reproducibility,Cooperating Robots
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要