Neuro-Symbolic Learning For Bilevel Robot Planning (Thesis Defense)

Speaker: Tom Silver , CSAIL / EECS

Date: Wednesday, April 03, 2024

Time: 3:00 PM to 4:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location:

Event Type: Thesis Defense

Room Description:

Host:

Contact: Thomas Scott Silver, tslvr@csail.mit.edu

Relevant URL:

Speaker URL: https://web.mit.edu/tslvr/www/

Speaker Photo:
Tomsilver2 high res

Reminders to:

Reminder Subject: TALK: Neuro-Symbolic Learning For Bilevel Robot Planning (Thesis Defense)

Location: 32-G449 (Patil/Kiva)

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 defense, I will give an overview of my PhD 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, I will also briefly discuss planning to learn, where the robot uses planning to collect additional data that it can use to improve its abstractions. My 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.

Research Areas:
AI & Machine Learning, Robotics

Impact Areas:

This event is not part of a series.

Created by Thomas Scott Silver Email at Wednesday, March 27, 2024 at 11:58 AM.