CS Seminar: Tom Silver

Description

Tom Silver, a final-year doctoral student at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science, will give a talk titled "Learning and Planning with Relational Abstractions" for the Department of Computer Science.

This is a hybrid event; to attend virtually, use the Zoom link provided on the department website.

Abstract:

Decision-making in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning, where decision-making is decomposed into reasoning about "what to do" (task planning) and "how to do it" (continuous optimization). Bilevel planning is powerful, but it requires multiple types of domain-specific abstractions that are often difficult to design by hand. In this talk, Tom Silver will give an overview of his work on learning these abstractions from data; this work represents the first unified system for learning all the abstractions needed for bilevel planning. In addition to learning to plan, he will also discuss planning to learn, where the robot uses planning to collect additional data that it can use to improve its abstractions. His long-term goal is to create a virtuous cycle where learning improves planning and planning improves learning, leading to a very general library of abstractions and a broadly competent robot.