Learning Diffusion Models to Enable Efficient Sampling for Task and Motion Planning on a Panda Robot

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.

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

2025

Author dc:creator

• Johnson, Quincy

Advisors dc:contributor.advisor

• Kaelbling, Leslie
• Mendez-Mendez, Jorge

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/1721.1/159141

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/159141