# Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots

arXiv: https://arxiv.org/abs/2503.01471 · HTML: https://mlsanity.com/paper/2503.01471/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2025.

## Built on by (most cited first, 15 of 15)

- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Reinforcement Learning for Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2602.01266/) (2026)
- [DiffAero: A GPU-Accelerated Differentiable Simulation Framework for Efficient Quadrotor Policy Learning](https://mlsanity.com/paper/2509.10247/) (2025)
- [MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects](https://mlsanity.com/paper/2507.21796/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)
- [EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents](https://mlsanity.com/paper/2605.01250/) (2026)
- [Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX](https://mlsanity.com/paper/2606.01478/) (2026)
- [Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty](https://mlsanity.com/paper/2606.16621/) (2026)
- [RL-based Control of UAS Subject to Significant Disturbance](https://mlsanity.com/paper/2504.08114/) (2025)
- [Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking](https://mlsanity.com/paper/2506.17823/) (2025)
- [aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones](https://mlsanity.com/paper/2602.07264/) (2026)
- [Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements](https://mlsanity.com/paper/2603.22182/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)
- [Agile Fall Recovery for Quadrotors with Bidirectional Thrust via Reinforcement Learning](https://mlsanity.com/paper/2606.16513/) (2026)
- [Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2606.20491/) (2026)

## Abstract (arXiv)

This paper contributes the Aerial Gym Simulator, a highly parallelized, modular framework for simulation and rendering of arbitrary multirotor platforms based on NVIDIA Isaac Gym. Aerial Gym supports the simulation of under-, fully- and over-actuated multirotors offering parallelized geometric controllers, alongside a custom GPU-accelerated rendering framework for ray-casting capable of capturing depth, segmentation and vertex-level annotations from the environment. Multiple examples for key tasks, such as depth-based navigation through reinforcement learning are provided. The comprehensive set of tools developed within the framework makes it a powerful resource for research on learning for control, planning, and navigation using state information as well as exteroceptive sensor observations. Extensive simulation studies are conducted and successful sim2real transfer of trained policies is demonstrated. The Aerial Gym Simulator is open-sourced at: https://github.com/ntnu-arl/aerial_gym_simulator.