Learning Diffusion Models to Enable Efficient Sampling for Task and Motion Planning on a Panda Robot
Name
johnson-qjohnson-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
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5.06 MB
Format
Adobe PDF
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ece0d8da359f9961147d62fdab4b4d20
Author(s)
Johnson, Quincy
Advisor(s)
Kaelbling, Leslie
Mendez-Mendez, Jorge
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
Abstract
A search then sample approach to bilevel planning in the context of task and motion planning is one method of effectively solving multi-step robotics problems. In this planning framework, high-level plans of abstract actions are refined into low-level continuous transitions by sampling controller parameters associated with each action. Efficiently sampling these parameters remains a significant challenge, as exhaustive searches often become computational bottlenecks, especially for tasks requiring complex or multimodal parameter distributions. Moreover, relying on samplers hand-designed by humans is both impractical and limiting. To address these challenges, we propose using diffusion models to learn efficient sampling distributions from demonstrations. By avoiding the limitations of hand-specified and naïve sampling methods, our approach enhances planning efficiency and achieves superior performance across diverse tasks that require learning multimodal parameter distributions to solve successfully.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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