Characterizing the Epistemic Uncertainty of Predictive Action Models and Sampling-Based Motion Planners for Robotic Manipulation

We derive methods to represent the epistemic uncertainty of models used in long-horizon robot planning problems in autonomous manipulation. We develop a representation of epistemic uncertainty for two types of models: uncertainty over the physical parameters of a model that predicts the observed outcome of a manipulation action and uncertainty over a geometric graph built by a sampling-based motion planner as a representation of the configuration space to answer a motion planning query. We propose a simple planning system that integrates these uncertainty characterizations to reason about the informational value of executing a manipulation action or allocating a number of samples to a sampling-based motion planner.

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

• Shaw, Seiji A.

Advisor dc:contributor.advisor

• Roy, Nicholas

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/158490

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/158490