# Aerial Gym -- Isaac Gym Simulator for Aerial Robots

arXiv: https://arxiv.org/abs/2305.16510 · HTML: https://mlsanity.com/paper/2305.16510/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 11 of 11)

- [OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control](https://mlsanity.com/paper/2309.12825/) (2023)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [Task-driven Compression for Collision Encoding based on Depth Images](https://mlsanity.com/paper/2309.05289/) (2023)
- [RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation](https://mlsanity.com/paper/2502.02054/) (2025)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation](https://mlsanity.com/paper/2505.12428/) (2025)
- [Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor](https://mlsanity.com/paper/2501.18490/) (2025)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2407.00889/) (2024)
- [Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2602.07101/) (2026)
- [GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo](https://mlsanity.com/paper/2609.13243/) (2026)

## Abstract (arXiv)

Developing learning-based methods for navigation of aerial robots is an intensive data-driven process that requires highly parallelized simulation. The full utilization of such simulators is hindered by the lack of parallelized high-level control methods that imitate the real-world robot interface. Responding to this need, we develop the Aerial Gym simulator that can simulate millions of multirotor vehicles parallelly with nonlinear geometric controllers for the Special Euclidean Group SE(3) for attitude, velocity and position tracking. We also develop functionalities for managing a large number of obstacles in the environment, enabling rapid randomization for learning of navigation tasks. In addition, we also provide sample environments having robots with simulated cameras capable of capturing RGB, depth, segmentation and optical flow data in obstacle-rich environments. This simulator is a step towards developing a - currently missing - highly parallelized aerial robot simulation with geometric controllers at a large scale, while also providing a customizable obstacle randomization functionality for navigation tasks. We provide training scripts with compatible reinforcement learning frameworks to navigate the robot to a goal setpoint based on attitude and velocity command interfaces. Finally, we open source the simulator and aim to develop it further to speed up rendering using alternate kernel-based frameworks in order to parallelize ray-casting for depth images thus supporting a larger number of robots.