Event Details

PhD Dissertation Defense - Jesse Zhang

Tue, May 06, 2025
5:00 PM - 6:30 PM
Location: RTH 306
Title: Scaling Robot Adaptation with Large Model Guidance
 
Date: Tuesday, May 6, 2025  
 
Time: 5:00 pm - 6:30 pm   Location: RTH (Ronald Tutor Hall), room 306  
 
Defense Committee Members: Erdem Biyik (Chair), Jesse Thomason, Joseph J. Lim, Feifei Qian, Gaurav Sukhatme
 
Abstract: General-purpose robots deployed in the real world must respond to dynamic environments and continuously learn new tasks. However, existing methods struggle to support such adaptation at scale—that is, without substantial human supervision. In this talk, I present an approach to scalable robot adaptation by leveraging the general knowledge encoded in Large Pre-trained Models (LPTMs). I show how integrating LPTMs with robot learning frameworks can: (1) enhance robot pre-training to better prepare for unfamiliar tasks and settings, (2) adapt to new tasks and environments with human feedback, and (3) ultimately enable autonomous adaptation with minimal human input. Together, these contributions outline a path toward generalizable algorithms that empower robots to learn novel tasks in real-world, unstructured environments.