Yunfan's research focuses on developing scalable approaches to building generalist robots for everyday tasks. He works on robot foundation model training, continual learning, learning from human data, real-to-sim-to-real transfer, and human-robot collaboration, leveraging large-scale computation and data to achieve generalizable and robust robotic behavior.
Yunfan is a third-year PhD candidate in the Computer Science Department at Stanford University, advised by Fei-Fei Li. His research interests span robot learning, reinforcement learning, and embodied AI, with a focus on developing scalable approaches to building generalist robots for everyday tasks. His work has been recognized with the NeurIPS 2022 Outstanding Paper Award and the IEEE ICRA 2024 Best Conference Paper Award.