I am a first-year CS Ph.D. student at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), advised by Prof. Guiliang Liu. I received my B.Eng. in Automation (ranking in the top 2%) from Harbin Institute of Technology, Shenzhen in 2025. I have also worked as a research intern at Shanghai AI Laboratory, mentored by Ming Zhou.
My work has been published in top-tier AI and robotics venues, such as ICML, ICLR, and ICRA. These projects span robot learning, humanoid locomotion, robotic manipulation, and embodied AI.
My research focuses on building embodied agents that learn through interaction and exhibit robust behaviors in real-world environments. In particular, I am interested in reinforcement learning for robotic control, robot representation learning, and test-time steering, with the broader goal of making physical intelligence more general, reliable, and deployable.
News
Selected Publications
HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
A hierarchical whole-body control approach for robust humanoid locomotion across diverse terrains, robot structures, and disturbance settings.
RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation
A 4D flow representation and policy learning framework for robotic manipulation.
A dynamic grouping residual reinforcement learning framework for adaptive embodied policies.
A bidirectional diffusion framework for offline reinforcement learning that models both future and history trajectories from intermediate states.
SignBot: Learning Human-to-Humanoid Sign Language Interaction
A human-to-humanoid sign language interaction framework spanning motion retargeting, motion control, and generative interaction.
A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
A vision-language-action-critic model that provides dense progress rewards and action generation for real-world robot reinforcement learning.
SMAP: Self-supervised Motion Adaptation for Physically Plausible Humanoid Whole-body Control
A self-supervised motion adaptation framework for physically plausible humanoid whole-body control.
MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion
A multi-agent reinforcement learning formulation that treats the legs of a single quadruped robot as cooperating agents.
Experience
Research internship on embodied intelligence and robotics.
Worked on vision-language-action models and reinforcement fine-tuning.
Undergraduate research on robotics and reinforcement learning.
Remote research collaboration on embodied AI and robotics.
Education
- 2025 - Present, Ph.D. in Computer Science, The Chinese University of Hong Kong, Shenzhen.
- Jul. 2024 - Sep. 2024, Visiting Student, Westlake University.
- Jul. 2023 - Sep. 2023, Visiting Student, University of Oxford.
- 2021 - 2025, B.Eng. in Automation, Harbin Institute of Technology, Shenzhen. Rank: top 2%.
Honors & Awards
- Dec. 2025: 🏆 Top 1.3% (ranked 6th of 463 teams) in the Tencent AI Arena Global Open Competition, Reinforcement Learning Embodied-AI Track (award: CNY 15,000).
- 2025-2029: 🏅 Yongping Duan Scholarship (CNY 15,000/month).
- 2023-2024: 🏅 National Scholarship (top 0.2% of students at Chinese universities).
- 2023-2024: 🏆 The 18th National University Students Intelligent Car Race, National Second Prize.
- 2022-2024: 🏅 First-Class Scholarship for Undergraduate Students (top 5%; awarded twice; total: CNY 12,000).
- 2021-2022: 🏅 Second-Class Scholarship for Undergraduate Students (CNY 4,000).
- 2021-2023: 🏅 Outstanding League Member (2 times).
- 2021-2023: 🏅 Outstanding Student (2 times).
Teaching
- Teaching Assistant, CSC-1004: Computational Laboratory Using Java, The Chinese University of Hong Kong, Shenzhen.
Service
- Reviewer: NeurIPS, ICML, ICLR, ICRA, IROS.