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[IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2)
| Date | Stars |
|---|---|
| 2026-07-24 | 1553 |
| 2026-07-25 | 1553 |
| 2026-07-28 | 1555 |
| 2026-07-30 | 1555 |
| 2026-08-06 | 1555 |
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# NavRL: Learning Safe Flight in Dynamic Environments
[](https://docs.python.org/3/whatsnew/3.10.html)
[](https://wiki.ros.org/noetic)
[](https://docs.ros.org/en/humble/index.html)
[](https://docs.omniverse.nvidia.com/isaacsim/latest/overview.html)
[](https://releases.ubuntu.com/22.04/)
Welcome to the NavRL repository! This repository provides the implementation of the [NavRL](https://ieeexplore.ieee.org/document/10904341) framework, designed to enable robots to safely navigate dynamic environments using Reinforcement Learning. While the original paper focuses on UAV navigation, the NavRL can be extended to any robot that adopts a velocity-based control system.
<table>
<tr>
<td><img src="media/NavRL-demo1.gif" style="width: 100%;"></td>
<td><img src="media/NavRL-demo2.gif" style="width: 100%;"></td>
<td><img src="media/NavRL-demo3.gif" style="width: 100%;"></td>
</tr>
</table>
For additional details, please refer to the related paper available here:
Zhefan Xu, Xinming Han, Haoyu Shen, Hanyu Jin, and Kenji Shimada, "NavRL: Learning Safe Flight in Dynamic Environments”, *IEEE Robotics and Automation Letters (RA-L)*, 2025. [\[IEEE Xplore\]](https://ieeexplore.ieee.org/document/10904341) [\[preprint\]](https://arxiv.org/pdf/2409.15634) [\[YouTube\]](https://youtu.be/EbeJW8-YlvI) [\[BiliBili\]](https://www.bilibili.com/video/BV1gsA9eTErz/?share_source=copy_web&vd_source=1333db331406abb1b5d4cece1e253427)
## News
* **2025-04-06:** We release easy-to-run Python scripts that allows users to quickly run demos.
* **2025-02-23:** The GitHub code, video demos, and relavant papers for our NavRL framework are released. The authors will actively maintain and update this repo!
## Table of Contents
- [Training in NVIDIA Isaac Sim](#I-Training-in-NVIDIA-Isaac-Sim)
- [Deployment Virtual Environment](#II-Deployment-Virtual-Environment)
- [NavRL ROS1 Deployment](#III-NavRL-ROS1-Deployment)
- [NavRL ROS2 Deployment](#IV-NavRL-ROS2-Deployment)
- [Citation and Reference](#V-Citation-and-Reference)
- [Acknowledgement](#VI-Acknowledgement)
## NavRL Quick Demos in 3 Minutes
We provide a pretrained model and easy-to-run Python scripts for quick demos of the NavRL framework.
<table>
<tr>
<td><img src="media/simple-navigation.gif" style="width: 100%;"></td>
<td><img src="media/dynamic-navigation.gif" style="width: 100%;"></td>
<td><img src="media/multi-robot-navigation.gif" style="width: 100%;"></td>
</tr>
</table>
To get started, please follow the steps in [Deployment Virtual Environment](#II-Deployment-Virtual-Environment) to set up the Conda environment. Once the setup is complete, you can run the following three demos with the following commands:
```
conda activate NavRL
cd NavRL/quick-demos
# DEMO I: Navigating to a predefined goal point
python simple-navigation.py
# DEMO II: Navigating to dynamically/randomly assigned goal points
python random-navigation.py
# DEMO III: Multi-robot navigation
python multi-robot-navigation.py
```
## I. Training in NVIDIA Isaac Sim
This section provides the steps for training your own RL agent with the NavRL framework in Isaac Sim. **If you are not interested in training the agent yourself, feel free to skip this section and jump straight to the deployment section.**
### Isaac Sim Installation
This project was developed using **Isaac Sim version 2023.1.0-hotfix.1**, released in November 2023. **Please make sure you download and use this exact version, as using a different version may lead to errors due to version incompatibility.** Also, ensure that you have [conda](https://docs.anaconda.com/miniconda/) installed.
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Zhefan Xu · Carnegie Mellon University · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:db478beeeb377f14, topic:robotics, topic:embodied-ai, readme:robotics
matched fp:db478beeeb377f14, topic:reinforcement-learning, readme:reinforcement learning