I am a Ph.D. candidate in the Robotics and Automation Group at the Department of Mechanical and Industrial Engineering. I am interested in how we can make robots that are more intelligent and can help us. 

To advance this field, I have focused on Imitation Learning, as it provides a highly general framework for endowing robots with human-like skills. On the other hand, generative modeling has emerged as a compelling direction, as models for image, text, and video have proven capable of consuming and generalizing from large quantities of data. However, there remains a significant gap between these modeling tasks and the challenge of controlling a robot in an ever-changing, chaotic world. The main motivation of my research is to bridge this gap.

Also, check out my fireside chat with Sergey Levine for the Workshop on Learning from Diverse, Offline Data at RSS 2022.