Zhouheng Li

Zhouheng Li

Ph.D. student @ZJU.

Zhouheng Li

I am currently a 4th-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. I was previously advised on energy model-based composition by Prof. Yilun Du from Harvard University. I am currently collaborating closely on car racing-related research with Dr. Mattia Piccinini and Dr. Baha Zarrouki at the AVS Lab, Technical University of Munich (TUM).
Starting in May 2026, I will join the AutoMan Lab at Nanyang Technological University (NTU) as a visiting researcher, focusing on VLA-enhanced multi-agent game planning for autonomous racing, under the supervision of Prof. Chen Lyu.
✨ Enjoyments of life: 🎾 tennis, 🎹 piano, 🎲 board games (Seven Wonders: Duel, Splendor, etc), hiking, ping-pong, traveling. 🚀 I am on the job market now! Please contact me if you have any relevant positions or opportunities (📨 zh_li@zju.edu.cn).

🎯 Research

My research focuses on developing embodied robots that can make intelligent decisions and plan effectively in highly dynamic scenarios, particularly when operating at the limits of handling (📝 Publications). To this end, I actively integrate physics-informed generative models with model-based approaches, such as Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI). Currently, I am especially interested in planning strategies for autonomous car racing and drone racing, with a focus on the following directions:
⭐ Game Planning for Competitive Multi-Agent Interaction: Game theory provides a principled framework for modeling competition over limited resources, yet key challenges remain in computational efficiency and safe strategy transitions. My research leverages generative models as priors to improve warm-starting and adaptation, enabling stable and robust motion planning in complex multi-agent environments.
⭐ World Model and Feedback Control-based VLA: A key limitation of current world models is that their predictions often lack essential physical consistency. Feedback control can inject physical information into the prediction process, while improved predictions in turn enhance control performance, resulting in more robust long-horizon behavior.
⭐ Multimodal Generative Models: Integrating diverse sensory modalities while balancing multiple objectives remains challenging for robotic systems. This direction focuses on leveraging energy-based models and classifier-free guidance to enable flexible composition of image and state modalities at inference time, supporting efficient adaptation and strong few-shot and zero-shot generalization.
If any of these topics caught your interest, feel free to drop me emails. I enjoy collaborating on interesting projects and making amazing things happen together!

✨ Highlights

Demos showcasing the effectiveness of my related work across distinct challenges: (i) vision-based drone racing with neural signed distance fields; (ii) car racing with velocity-enhanced MPCC; (iii) energy-based model composition at inference time for safe planning; and (iv) physics-informed BEV world modeling for consistent long-horizon prediction. Click the buttons below to explore the details of each project!

Vision-based Drone Racing

Slow Motion replay
Orientation Mismatch replay
Position Mismatch replay
Real-time vision-guided drone racing with neural SDFs. Real-world trajectories on a circular track with perturbed gates, where the quadrotor navigates successfully using only onboard depth perception.
Web
PDF

Velocity-Enhanced Car Racing

racing
Implementation of the proposed EVO-MPCC planner on the Roboracer platform. The local planner achieves feasible and aggressive cornering through a sharp U-turn at 11.5 km/h.
Web
PDF
Code

Energy-based Diffusion Navigation

Static Model replay
Dynamic Model replay
Composed Result replay
This energy-parameterized diffusion composition guarantees collision-free trajectory planning in unseen scenes. Energy-parameterized diffusion learns a conservative energy field, enabling flexible integration of multiple constraints and generalization to previously unseen environments without retraining.
Web
PDF
MPC
ItCA

Physics-Informed BEV World Model

Baseline with Eat up (DIAMOND) replay
Baseline with Eat up (DIAMOND) replay
Soft Mask with Consistency replay
Soft Mask with Consistency replay
The Physics-Informed World Model is a lightweight generative model that predicts future images conditioned on the current scene and control inputs. By incorporating the relative distance between the ego vehicle and surrounding vehicles via a soft mask as an additional conditioning signal, it effectively captures inter-vehicle interactions, thereby enabling forecasting with strong existential and temporal consistency in dynamic environments.
Web
PDF
Code

🔥 News

📝 Selected Publications

    * means equal contribution.

  • EVO-MPCC: Enhanced Velocity Optimization with Learning-Based Auto-Tuning for Real-Time Vehicle Trajectory Planning

    Zhouheng Li, Bei Zhou, Mattia Piccinini, Cheng Hu, Baha Zarrouki, Rahul Mangharam, Lei Xie

    Code

  • Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields

    Fangguo Zhao, Hanbing Zhang, Zhouheng Li, Xin Guan, Shuo Li

  • Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition

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

    Code

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

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

    Code

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

    Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

    Code

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

    Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

    Code

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

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

  • Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles

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

  • Enhancing Physical Consistency in Lightweight World Models

    Dingrui Wang*, Zhexiao Sun*, Zhouheng Li, Cheng Wang, Youlun Peng, Hongyuan Ye, Baha Zarrouki, Wei Li, Mattia Piccinini, Lei Xie, Johannes Betz

    Code

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

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

  • 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

  • An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency

    Wule Mao, Zhouheng Li, Lei Xie, Hongye Su

🏁 Competitions


💡 Services

  • The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’ 26).
  • IEEE International Conference on Robotics and Automation (ICRA’ 26).
  • Robotics and Autonomous Systems.

🤗 So Glad You're Here!

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