Characterizing the Epistemic Uncertainty of Predictive Action Models and Sampling-Based Motion Planners for Robotic Manipulation
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shaw-seijis-sm-eecs-2024-thesis.pdf
Description
Thesis PDF
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5.34 MB
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Adobe PDF
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Author(s)
Shaw, Seiji A.
Advisor(s)
Roy, Nicholas
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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