# One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms

arXiv: https://arxiv.org/abs/2504.21586 · HTML: https://mlsanity.com/paper/2504.21586/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors](https://mlsanity.com/paper/1903.04628/) (2019)
- [Autonomous Drone Racing: A Survey](https://mlsanity.com/paper/2301.01755/) (2023)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [Learning a Single Near-hover Position Controller for Vastly Different Quadcopters](https://mlsanity.com/paper/2209.09232/) (2022)
- [Domain Randomization via Entropy Maximization](https://mlsanity.com/paper/2311.01885/) (2023)
- [End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight](https://mlsanity.com/paper/2311.16948/) (2023)
- [Learning Minimum-Time Flight in Cluttered Environments](https://mlsanity.com/paper/2203.15052/) (2022)
- [Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks](https://mlsanity.com/paper/2206.06282/) (2022)
- [End-to-end Neural Network Based Quadcopter control](https://mlsanity.com/paper/2304.13460/) (2023)
- [Guidance & Control Networks for Time-Optimal Quadcopter Flight](https://mlsanity.com/paper/2305.02705/) (2023)
- [Flying a Quadrotor with Unknown Actuators and Sensor Configuration](https://mlsanity.com/paper/2409.01080/) (2024)

## Built on by (most cited first, 7 of 7)

- [RAPTOR: A Foundation Policy for Quadrotor Control](https://mlsanity.com/paper/2509.11481/) (2025)
- [SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2510.14783/) (2025)
- [Agile Flight Emerges from Multi-Agent Competitive Racing](https://mlsanity.com/paper/2512.11781/) (2025)
- [AirDreamer: Generalist Drone Navigation with World Models](https://mlsanity.com/paper/2606.03252/) (2026)
- [Learning Agile Gate Traversal via Analytical Optimal Policy Gradient](https://mlsanity.com/paper/2508.21592/) (2025)
- [MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers](https://mlsanity.com/paper/2603.10714/) (2026)
- [Embodiment-conditioned Generalist Control for Multirotor Aerial Robots](https://mlsanity.com/paper/2606.10857/) (2026)

## Abstract (arXiv)

In high-speed quadcopter racing, finding a single controller that works well across different platforms remains challenging. This work presents the first neural network controller for drone racing that generalizes across physically distinct quadcopters. We demonstrate that a single network, trained with domain randomization, can robustly control various types of quadcopters. The network relies solely on the current state to directly compute motor commands. The effectiveness of this generalized controller is validated through real-world tests on two substantially different crafts (3-inch and 5-inch race quadcopters). We further compare the performance of this generalized controller with controllers specifically trained for the 3-inch and 5-inch drone, using their identified model parameters with varying levels of domain randomization (0%, 10%, 20%, 30%). While the generalized controller shows slightly slower speeds compared to the fine-tuned models, it excels in adaptability across different platforms. Our results show that no randomization fails sim-to-real transfer while increasing randomization improves robustness but reduces speed. Despite this trade-off, our findings highlight the potential of domain randomization for generalizing controllers, paving the way for universal AI controllers that can adapt to any platform.