About Me
I am a first-year graduate student at Zhejiang University (ZJU), majoring in Control Science and Engineering. I am fortunate to be advised by Prof. Fei Gao. My research interests include: Reinforcement Learning, Robotics Control, World Models. I am always open to discussions and collaborations. If you are interested, please feel free to contact me via email (xijiehuang@zju.edu.cn).
Earlier, I received my Bachelor’s degree from Dalian University of Technology (DUT) in 2025. I have secured National Prizes in the following three competitions: the National Undergraduate Smart Car Race, the National Undergraduate Electronics Design Contest, and the National Undergraduate Embedded System Design Contest.
🔥 News
- 2026.02: 🎉🎉 Flying in Clutter on Monocular RGB via 3D Radiance Fields is accepted to RAL!
- 2025.08: 🎉🎉 Reactive Aerobatic Flight via Reinforcement Learning is accepted to RAL!
🎖️ Honors and Awards
- 2023.09, National Scholarship.
📖 Educations
- 2025.09 - Present, M.S. in Control Science and Engineering, Zhejiang University.
- 2021.09 - 2025.06, B.S. in Instrument Science and Technology, Dalian University of Technology.
📚 Publications
NavDreamer: Video Models as Zero-Shot 3D Navigators
Xijie Huang, Weiqi Gai, Tianyue Wu, Congyu Wang, Zhiyang Liu, Xin Zhou, Yuze Wu, and Fei Gao
A zero-shot 3D navigation framework that leverages video generation models to predict future observations and guide the robot.
Flying in Clutter on Monocular RGB by Learning in 3D Radiance Fields with Domain Adaptation
Xijie Huang, Jinhan Li, Tianyue Wu, Xin Zhou, Zhichao Han, and Fei Gao
This work presented a method for autonomous high-speed flight in complex environments using only monocular RGB cameras, trained within 3DGS.
Reactive Aerobatic Flight via Reinforcement Learning
Zhichao Han, Xijie Huang, Zhuxiu Xu, Jiarui Zhang, Yuze Wu, Mingyang Wang, Tianyue Wu, and Fei Gao
A reinforcement learning approach for reactive aerobatic maneuvers, enabling drones to perform agile tasks in constrained spaces.
💬 Competitions
🏆 National First Prize : The National Undergraduate Smart Car Race
A high-inertia hovercraft-style mobile vessel. Designed the circuit hardware and control algorithms.
🏆 National First Prize : The National Undergraduate Embedded System Design Contest
A dual-mode land-air drone. Designed the circuit hardware and flight control algorithms.
🏆 National First Prize : The National Undergraduate Smart Car Race
Wireless charging-based dual-car power relay. Designed the vision algorithms.
🏆 National Second Prize : The National Undergraduate Electronics Design Contest
Dual-gimbal tracking and target shooting. Designed the control and vision recognition algorithms.