School of CSE Seminar Series: Sherry Yang

Speaker: Sherry Yang, assistant professor at New York University
Date and Time: January 16, 2:00-3:00 p.m.
Location: Coda 114
Host: Bo Dai

Title: Evaluating and Improving Policies in a World Model 

Abstract: Given a low-cost environment with accurate dynamics and reward, we have trained agents that can achieve superhuman performance (e.g., AlphaGo, LLM for ICPC). However, robot interactions with the physical world incurs high cost, making learning  physical agents difficult. In this talk, we will discuss how to learn a world model from large-scale real-robot interaction data. We then discuss how the world model can be used to evaluate real-robot policies efficiently and effectively, including testing out-of-distribution settings with novel objects and distractors. Lastly, we will discuss how to use the world model to perform reinforcement learning and planning to further improve robot policies as physical agents. 

Bio: Sherry Yang is an Assistant Professor of Computer Science at NYU Courant and a Staff Research Scientist at Google DeepMind. She researches in machine learning with a focus on reinforcement learning and generative modeling. Her current research interests include learning world models and agents, and their applications in robotics and AI for science. Her research has been recognized by the Best Paper award at ICLR and various media outlets such as VentureBeat and TWIML. She has organized tutorials, workshops, and served as Area Chairs at major conferences (NeurIPS, ICLR, ICML, CVPR). Prior to her current role, she was a post-doc at Stanford working with Percy Liang. She received her Ph.D. from UC Berkeley advised by Pieter Abbeel and master’s and bachelor’s degrees from MIT.