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 from Harbin Institute of Technology, Shenzhen in 2025, ranking in the top 2% of my cohort.
My research focuses on building embodied agents that can learn from interaction, adapt to new situations, and transfer robust behaviors across real-world settings. I am particularly interested in reinforcement learning for robotic control, robot representation learning, and test-time steering/adaptation.
My work has appeared in top-tier AI and robotics venues, including ICML, ICLR, and ICRA.
I have also been fortunate to work as a research intern at Shanghai AI Laboratory, mentored by Ming Zhou.
At a high level, I enjoy working on algorithms and systems that make 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.