Object-Centric Planning for Long-Horizon Robotic Manipulation and Navigation
Name
curtis-curtisa-sm-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
36.77 MB
Format
Adobe PDF
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782e375e535b80772741fe19259df6f0
Author(s)
Curtis, Aidan
Advisor(s)
Kaelbling, Leslie P.
Lozano-Pérez, Tomás
Tenenbaum, Joshua B.
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
A primary objective within the robotics research community is the development of robotic agents capable of executing long-horizon tasks within complex and novel environments. The sparse and factored nature of object-centric planning makes it a good candidate for the reasoning engine inside such an agent. However, several challenges remain under an object-centric planning framework. Challenges arise in areas such as efficiently grounding states with novel objects in cluttered environments, maintaining efficiency under large object sets, and safe exploration and manipulation in partially observable and nondeterministic environments. This thesis examines these limitations and proposes several strategies for solving them while maintaining the generalizability and flexibility of object-centric planning in long-horizon tasks.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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