Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910035267813376 |
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| author | Sun, Fangyu Li, Fanxing Hu, Yu Zhang, Linzuo Liu, Yueqian Yu, Wenxian Zou, Danping |
| author_facet | Sun, Fangyu Li, Fanxing Hu, Yu Zhang, Linzuo Liu, Yueqian Yu, Wenxian Zou, Danping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_24030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles Sun, Fangyu Li, Fanxing Hu, Yu Zhang, Linzuo Liu, Yueqian Yu, Wenxian Zou, Danping Robotics 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. |
| title | Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.24030 |