One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms (2504.21586v1)

Abstract: 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.

Overview of "One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms"

The paper "One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms" addresses the enduring challenge in quadcopter racing of developing a single neural network controller that effectively generalizes across various quadcopter platforms. The significance of this research lies in its aim to bridge the capability gap where current AI systems often overfit to specific models and fail to generalize effectively across other drones. By leveraging domain randomization, the paper presents a neural network controller capable of performing robustly on physically distinct quadcopters, specifically 3-inch and 5-inch drones.

Domain Randomization Methodology

Central to the research is the technique of domain randomization (DR), which involves training the controller in simulated environments with varied parameters. DR is employed to enhance the robustness and generalization of the control policies when transferred from simulation to real-world scenarios. The strategy contrasts conventional approaches that require platform-specific tuning and precise modeling. The paper implements DR by randomizing key model parameters and evaluates how different extents of randomization affect the performance and adaptability of the controller.

Results and Performance Analysis

The empirical evaluation of the proposed neural network controller was conducted through real-world tests on two markedly different quadcopter models. The results were telling in highlighting the trade-off inherent in the DR approach: increasing randomization improves robustness and adaptability across different drones but results in a slight reduction in speed compared to controllers fine-tuned for a specific platform. Notably, the generalized controller demonstrated mean episode rewards that were comparable to those of fine-tuned models, with slightly lower crash rates indicative of better adaptation capabilities.

The extensive simulation experiments confirm the effectiveness of the generalized controller, with further analysis indicating that the benefits of domain randomization are quantitatively significant. The general controller showed successful sim-to-real transferability, a rare achievement in autonomous drone racing AI, where many state-of-the-art solutions face hurdles in transitioning learned policies into the physical world intact.

Discussion and Implications

The implication of successfully demonstrating a generalizable controller through domain randomization is multifaceted. Practically, it suggests a path towards developing universal AI controllers that can efficiently operate across multiple drones, potentially revolutionizing applications requiring rapid deployment of autonomous drone fleets in varied environments. Theoretically, the adoption of a DR-based approach can be seen as a paradigm shift in drone AI, moving focus from high-precision modeling towards robustness and flexibility in policy training.

The paper importantly highlights the scalability of the approach, allowing further extensions into more complex racing scenarios or even other genres of autonomous UAV operations such as search and rescue or environmental monitoring. Future developments in this domain could involve integrating online adaptation capabilities within the network to refine it further in real-time, expanding the potential for quicker adaptation to unforeseen dynamics or specific mission requirements.

Conclusion

In conclusion, the paper presents a substantial contribution to the field of autonomous drones through the novel application of domain randomization for cross-platform generalization. Its contributions pave the way for more versatile and adaptable AI controllers capable of transforming dynamics in the field of autonomous racing and beyond. Future work could explore optimizing domain randomization techniques and exploring hybrid approaches that combine model-based and learning-based strategies tailored for agile and adaptive drone control in more diverse and challenging environments.