Neuro-Symbolic Learning for Bilevel Robot Planning

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. This thesis proposes the first unified system for learning all the abstractions needed for bilevel planning. Beyond learning to make planning possible, this thesis also considers learning to make planning fast, especially in environments with many objects. A final contribution considers planning to learn, where the robot iteratively plans online to collect additional data and then learns to improve planning. Altogether, the thesis represents a step toward a general-purpose robot that can autonomously synthesize a specialized library of abstractions and plan to solve a very broad set of tasks.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Silver, Tom

Advisors dc:contributor.advisor

• Kaelbling, Leslie Pack
• Tenenbaum, Joshua B.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156646

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156646