research

My goal is to build robots that are useful in everyday life, acting with dexterity, efficiency, and reliability in complex, changing environments.

Large-scale data has driven remarkable progress in robot learning. My research asks how robots can make the most of it: developing physical understanding that keeps fine-grained actions robust, and reasoning that transfers to new tasks.

Physically grounded multimodal representations

Effective dexterous manipulation requires understanding scene geometry and the dynamics of physical contact. I study how visual, geometric, and tactile information can be coherently integrated to connect perception, prediction, and action, enabling robots to anticipate and control their interactions with the world.

Scaling reasoning and learning through interaction

I aim to scale robots’ ability to solve novel tasks while reducing reliance on exhaustive demonstration coverage. I am interested in how foundation-model reasoning can guide planning and exploration, while reinforcement learning enables improvement through experience. Simulation supports scalable interaction, complemented by real-world feedback for adaptation and transfer.

Collaborations

At Peking University, I was advised by Prof. Yixin Zhu. I also collaborated with Dr. Siyuan Huang and Dr. Tengyu Liu at BIGAI, and Prof. Masayoshi Tomizuka at UC Berkeley.