Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles

• URL: http://arxiv.org/abs/2602.24030v1
• Date: Fri, 27 Feb 2026 13:56:59 GMT
• Title: Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
• Abstract summary: We propose a novel vision-based curriculum reinforcement learning framework for training a robust controller capable of addressing unseen obstacles in drone racing.<n>Our end-to-end control policy is implemented as a single network, allowing high-speed flight of quadrotors in environments with variable obstacles.
• Score: 12.806994271928483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous drone racing has attracted increasing interest as a research topic for exploring the limits of agile flight. However, existing studies primarily focus on obstacle-free racetracks, while the perception and dynamic challenges introduced by obstacles remain underexplored, often resulting in low success rates and limited robustness in real-world flight. To this end, we propose a novel vision-based curriculum reinforcement learning framework for training a robust controller capable of addressing unseen obstacles in drone racing. We combine multi-stage cu rriculum learning, domain randomization, and a multi-scene updating strategy to address the conflicting challenges of obstacle avoidance and gate traversal. Our end-to-end control policy is implemented as a single network, allowing high-speed flight of quadrotors in environments with variable obstacles. Both hardware-in-the-loop and real-world experiments demonstrate that our method achieves faster lap times and higher success rates than existing approaches, effectively advancing drone racing in obstacle-rich environments. The video and code are available at: https://github.com/SJTU-ViSYS-team/CRL-Drone-Racing.

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