Learning Compositional Abstract Models Incrementally for Efficient Bilevel Task and Motion Planning

In robotic domains featuring continuous state and action spaces, planning in long-horizon task is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. In this thesis, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. We use this surrogate objective in a hill-climbing search over predicate sets drawn from a grammar, which we call predicate invention. However, our research highlights another limitation in current symbolic operator learning techniques. They often fall short in robotics scenarios where the robot’s actions result in numerous inconsequential alterations to the abstract state. This limitation arises mainly because these techniques aim to precisely predict every observed change in that state, and as the execution horizon grows longer so does the built up complexity of the predictions. In this thesis, we study this separately and introduce an innovative method where the operators are induced to selectively predict by focusing solely on changes crucial for abstract planning to meet specific subgoals, which we call our operator learning procedure. Our contributions include: a predicate invention procedure based on a hill-climbing search over predicate sets, and a planning-driven operator learning objective based on a hill-climbing search algorithm that only model changes necessary for abstract planning and preserve compositionality of operators. We evaluate learning predicates and operators across a few toy environments and dozens of tasks from the demanding BEHAVIOR-100 benchmark.

Name thesis:degree_name

Master

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

• McClinton III, Willie B.

Advisors dc:contributor.advisor

• Kaelbling, Leslie Pack
• Lozano-Pérez, Tomás

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/1721.1/153869

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153869