PhD Thesis Proposal - Jesse Zhang

Title: Scalable Robot Adaptation with Large Pre-trained Models
 
Date and Time: 09/24/24 - 5:00p - 6:00p
 
Location: RTH 114
 
Committee Members: Erdem Biyik, Jesse Thomason, Joseph Lim, Daniel Seita, Somil Basil
 
Abstract: General robots deployed in the real world need to respond to dynamic environments and constantly learn new tasks. However, current approaches lack the ability to enable them to adapt to these ever-changing environments and tasks at scale, i.e., without extensive human supervision. My thesis proposal aims to tackle this problem by utilizing vast general knowledge stored in Large Pre-trained Models (LPTMs) to enable scalable and efficient robot adaptation. I will cover 3 fundamental paradigms in enabling robot adaptation: using LPTMs to (1) label offline data, (2) guide robots in learning new tasks online, and finally (3) adapt to new agent settings. Through extensive research in the first two paradigms and future thesis work proposed in the third, my proposal aims to produce general algorithms that will lead to robots mastering new tasks in unfamiliar environments with little human supervision.

Location: Ronald Tutor Hall of Engineering (RTH) - 114

Audiences: Everyone Is Invited

Contact: Jesse Zhang