Short Bio

🐣 Born in 2000, I grew up alongside the rise of intelligent machines. I received my B.Eng. degree from the School of Vehicle and Mobility, Tsinghua University, Beijing, China , in 2021. I am currently pursuing a Ph.D. in Mechanical Engineering at the same school. From 2025 to 2026, I am a visiting student scholar at the Department of Mechanical Engineering, UC Berkeley, California, United States . My research focuses on ⚙️ control theory, 🧠 reinforcement learning, and their applications in 🚗 autonomous vehicles and 🤖 robotics.

Curriculum Vitae

• 📄 My CV is available here: Guojian_Zhan_Resume_2604.pdf

👋 Welcome to @molumitu’s Personal Page

GPA & Awards

• 🎓 I received my B.Eng. degree from Tsinghua University with a GPA of 3.78/4.0.
• 🎓 I am currently pursuing my Ph.D. with a graduate GPA of 3.93/4.0.
• 🏅 I have been recognized with the Comprehensive Excellence Award and named an Outstanding Graduate of Tsinghua University.
• 🏅 I was awarded the 2025 National Graduate Scholarship 研究生国奖 (Top 0.2\% nationwide).

News & Updates

• 2026-04: Several papers are accepted by ICML 2026!
• 2026-02: MVP is selected as oral presentation (Top 1\%) by ICLR 2026!
• 2026-01: MVP is accepted by ICLR 2026!
• 2025-12: Revision of CTPG was submitted to TPAMI — hope for good luck!
• 2025-09: BOOM and MPGE are accepted by NeurIPS 2025!
• 2025-08: BPO is accepted by IEEE TNNLS!
• 2025-05: PINPE is accepted by IEEE RAL!

Reviewing & Organizing

• 📝 I am a reviewer for many conferences and journals in the fields of AI and robotics. (NeurIPS, ICML, ICLR, CoRL, ICRA, IROS, IEEE TNNLS, IEEE RAL, IEEE TITS, etc.)

📩 Contact Me:

• Email: zgj21@mails.tsinghua.edu.cn
• Email: zgj21@berkeley.edu
• Email: zishangzhan@gmail.com

Publications

Harmonized Dual Policy Improvement for Model-based Reinforcement Learning

Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation

Langevin Rollout Optimization for Modelic Reinforcement Learning

Wasserstein Generative Reinforcement Learning

Taming the Aleatoric Impulse in Off-Policy Reinforcement Learning

Off-policy Reinforcement Learning with Model-based Exploration Augmentation

Bootstrap Off-policy with World Model

Continuous-Time Policy Gradient

Bicriteria Policy Optimization for High Accuracy Reinforcement Learning

Enhanced DACER Algorithm with High Diffusion Efficiency

Physics Informed Neural Pose Estimation for Real-Time Shape Reconstruction of Soft Continuum Robots

Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios

Understanding Connection between PMP and HJB Equations from the Perspective of Hamilton Dynamics

Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots

PODAR: A Collision Risk Model Offering Valid Signals for Vehicular Interactions

Predictive Lagrangian Optimization for Constrained Reinforcement Learning

Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems

An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability

Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning

CIDC: A Flexible Formation Control Framework for Intelligent Connected Vehicles

Quantifying the Individual Differences of Drivers’ Risk Perception via Potential Damage Risk Model

Canonical Form of Datatic Description in Control Systems

A Transformation-Aggregation Framework for State Representation of Autonomous Driving Systems

Continuous-Time Policy Optimization

Data-Driven Safe Learning and Control with Safety Certificates

A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios

Enhance Generality by Model-based Reinforcement Learning and Domain Randomization

Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration

Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning

Languagempc: Large Language Models as Decision Makers for Autonomous Driving

Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections