I am currently a 3rd year Ph.D. student in the College of Control Science and Engineering at Zhejiang University , Hangzhou, China, under the supervision of Prof. Lei Xie and Prof. Hongye Su. Previously, I had the privilege of being mentored by Prof. Yilun Du from Harvard University. ✨ Enjoyments of life: 🎲 Board Games (Splendor, Seven Wonders: Duel, etc), 👣 hiking, 🎾 tennis, 🏓 ping-pong, 🗺️ traveling.

My ultimate goal is to develop embodied intelligent autonomous systems capable of seamlessly interacting with the physical world. (📝 Publications). To achieve this, my research focuses on motion planning techniques driven by physics-informed generative models, enabling safe generalization to out-of-distribution (OOD) scenarios. Currently, I am exploring planning strategies for both autonomous vehicle racing and drone racing, as well as advancing world models, with particular emphasis on the following key areas:
⭐ End-to-End Motion Planning for Unmanned Systems: End-to-end approaches can directly learn motion patterns from data distributions, effectively bypassing the need for complex planning and control (PnC) rules. Conditional diffusion models, informed by physical and task-specific information, enable stable and safe generalization in motion generation, thus providing strong guarantees for overall system performance.
⭐ Multimodal Generative Models: Multimodal inputs form the foundation of intelligent systems. By leveraging classifier-free guidance (CFG) for model composition, diverse modalities—such as images, language, and states—can be efficiently integrated, enabling the embodied intelligent motion generation.
⭐ Physics-Informed World Models: At present, world models still face challenges in maintaining temporal consistency during long-horizon sequence generation. By incorporating physical knowledge, world models can generate in latent spaces that adhere to physical laws, thereby ensuring stability and consistency in their predictions.
I am also actively involved in applying these techniques to Roboracer competition. If any of these topics caught your interest, feel free to drop me emails (📨 zh_li@zju.edu.cn). I enjoy collaborating on interesting projects and making amazing things happen together!

To demonstrate the effectiveness and robustness of my approaches, I showcase some demos that address distinct challenges: navigation, which emphasizes complex task and motion constraints, and racing, which requires highly integrated trajectory planning and control.

End2End Navigation

Static Model replay

Dynamic Model replay

Composed Result replay

Vehicle Racing

Implementation of the proposed VPMPCC planner on the F1TENTH platform. The safe and aggressive cornering velocity with in the sharp U-turn is 11.5 km/h.

^* means equal contribution.
• Under review

  

  Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
  

  Wule Mao^*, Zhouheng Li^*, Yunhao Luo, Yilun Du, Lei Xie

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  Mao W, Li Z, Luo Y, et al. Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition[J]. arXiv preprint arXiv:2507.04384, 2025.

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  @article{mao2025rapid, title={Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition}, author={Mao, Wule and Li, Zhouheng and Luo, Yunhao and Du, Yilun and Xie, Lei}, journal={arXiv preprint arXiv:2507.04384}, year={2025} }

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• ICRA 2025

  

  A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction
  

  Zhouheng Li, Bei Zhou, Cheng Hu, Lei Xie, Hongye Su

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  Li Z, Zhou B, Hu C, et al. A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction[J]. arXiv preprint arXiv:2410.11570, 2024.

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  @article{li2024data, title={A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction}, author={Li, Zhouheng and Zhou, Bei and Hu, Cheng and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.11570}, year={2024} }

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• RAS

  

  A rapid iterative trajectory planning method for automated parking through differential flatness
  

  Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

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  Li Z, Xie L, Hu C, et al. A rapid iterative trajectory planning method for automated parking through differential flatness[J]. Robotics and Autonomous Systems, 2024, 182: 104816.

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  @article{li2024rapid, title={A rapid iterative trajectory planning method for automated parking through differential flatness}, author={Li, Zhouheng and Xie, Lei and Hu, Cheng and Su, Hongye}, journal={Robotics and Autonomous Systems}, volume={182}, pages={104816}, year={2024}, publisher={Elsevier} }

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• ITSC 2024

  

  Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
  

  Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

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  Li Z, Xie L, Hu C, et al. Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method[C]//2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024: 1066-1073.

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  @inproceedings{li2024reduce, title={Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method}, author={Li, Zhouheng and Xie, Lei and Hu, Cheng and Su, Hongye}, booktitle={2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, pages={1066--1073}, year={2024}, organization={IEEE} }

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• IV 2024

  

  An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control
  

  Wangjia Weng, Cheng Hu, Zhouheng Li, Hongye Su, Lei Xie

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  Weng W, Hu C, Li Z, et al. An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control[C]//2024 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2024: 2749-2755.

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  @inproceedings{weng2024aggressive, title={An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control}, author={Weng, Wangjia and Hu, Cheng and Li, Zhouheng and Su, Hongye and Xie, Lei}, booktitle={2024 IEEE Intelligent Vehicles Symposium (IV)}, pages={2749--2755}, year={2024}, organization={IEEE} }

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• RAS

  

  Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
  

  Bei Zhou, Cheng Hu, Jun Zeng, Zhouheng Li, Johannes Betz, Lei Xie, Hongye Su

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  Zhou B, Hu C, Zeng J, et al. Adaptive learning-based model predictive control strategy for drift vehicles[J]. Robotics and Autonomous Systems, 2025: 104941.

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  @article{zhou2025adaptive, title={Adaptive learning-based model predictive control strategy for drift vehicles}, author={Zhou, Bei and Hu, Cheng and Zeng, Jun and Li, Zhouheng and Betz, Johannes and Xie, Lei and Su, Hongye}, journal={Robotics and Autonomous Systems}, pages={104941}, year={2025}, publisher={Elsevier} }

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• ITSC 2025

  

  A Learning-based Planning and Control Framework for Inertia Drift Vehicles
  

  Bei Zhou, Zhouheng Li, Lei Xie, Hongye Su, Johannes Betz

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  Zhou B, Li Z, Xie L, et al. A Learning-based Planning and Control Framework for Inertia Drift Vehicles[J]. arXiv preprint arXiv:2507.05748, 2025.

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  @article{zhou2025learning, title={A Learning-based Planning and Control Framework for Inertia Drift Vehicles}, author={Zhou, Bei and Li, Zhouheng and Xie, Lei and Su, Hongye and Betz, Johannes}, journal={arXiv preprint arXiv:2507.05748}, year={2025} }

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• IV 2025

  

  Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC
  

  Guoqiang Wu^*, Cheng Hu^*, Wangjia Weng, Zhouheng Li, Yonghao Fu, Lei Xie, Hongye Su

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  Wu G, Hu C, Weng W, et al. Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC[J]. arXiv preprint arXiv:2410.05740, 2024.

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  @article{wu2024learning, title={Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC}, author={Wu, Guoqiang and Hu, Cheng and Weng, Wangjia and Li, Zhouheng and Fu, Yonghao and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.05740}, year={2024} }

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• An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency
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  Mao W, Li Z, Xie L, et al. An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency[J]. arXiv preprint arXiv:2410.22643, 2024.

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  @article{mao2024overtaking, title={An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency}, author={Mao, Wule and Li, Zhouheng and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.22643}, year={2024} }

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  Wule Mao, Zhouheng Li, Lei Xie, Hongye Su

• 4th place in 18TH F1TENTH autonomous grand prix by IV 2024
  2024 IEEE Intelligent Vehicles Symposium (IV 2024), June 3rd - 5th 2024, Jeju Shinhwa World, Jeju Island, Korea
  Zhouheng Li, Cheng Hu, Bei Zhou, Yonghao Fu, Guoqiang Wu, Yangyang Xie
  

I truly appreciate everyone who takes the time to visit my homepage :)