Abstract: True gains of machine learning in AI sub-fields such as computer vision and natural language processing have come about from the use of large-scale diverse datasets for learning. In this talk, I will discuss how we can leverage large-scale diverse data in the form of egocentric videos (first-person videos of humans conducting different tasks) to similarly scale up policy learning for robots. A central challenge is the gap in embodiment and intentions. I will describe how we can leverage video data in spite of this gap by learning at different levels of abstraction. I will demonstrate applications of this principle for a) acquiring low-level visuomotor subroutines and high-level value functions for navigation, and b) building an interactive understanding of objects, through observation of human hands, for manipulation.
Biography: Saurabh Gupta is an Assistant Professor in the ECE Department at UIUC. Before starting at UIUC in 2019, he received his Ph.D. from UC Berkeley in 2018 and spent the following year as a Research Scientist at Facebook AI Research in Pittsburgh. His research interests span computer vision, robotics, and machine learning, with a focus on building agents that can intelligently interact with the physical world around them. He received the President’s Gold Medal at IIT Delhi in 2011, the Google Fellowship in Computer Vision in 2015, an Amazon Research Award in 2020, and an NSF CAREER Award in 2022.
