AI & Fundamentals

Generative World Modeling for Embodied Agents - Sherry Yang, Assistant Professor, NYU

Abstract:

Generative models have transformed content creation, and the next frontier may be simulating realistic experiences in response to actions by humans and agents. In this talk, I will talk about a line of work that involves learning a real-world simulator (i.e., a world model) to emulate interactions through generative modeling of video content. I will then talk about the applications of using this world model to train embodied agents through reinforcement learning (RL) and planning, which have demonstrated zero-shot real-world transfer. Lastly, I will talk about how to improve generative world models from real-world feedback.

Bio:

Sherry is an incoming assistant professor of Computer Science at NYU Courant and a staff research scientist at Google DeepMind. Sherry works on generative modeling from internet-scale data coupled with decision making algorithms such as imitation learning, planning, and reinforcement learning. Her research UniSim: Learning Interactive Real-World Simulators has been recognized by the Outstanding Paper award at ICLR. Sherry received her PhD in Computer Science at UC Berkeley and her Bachelor's and Master’s degree in Electrical Engineering and Computer Science at MIT.