SELECTIVE ACQUISITION OF OPERATOR KNOWLEDGE FOR SAFER SEMI-AUTONOMOUS ROBOT OPERATIONS

<title>ABSTRACT</title> <p> <italic>We have developed techniques for a robot to compute its expected myopic gain in performance from asking its operator specific questions, such as questions about how risky a particular movement action is around pedestrians. Coupled with a model of the operator’s costs for responding to inquiries, these techniques form the core of a new algorithm that iteratively allows the robot to decide what questions are in expectation most valuable to ask the operator and whether their value justifies potentially interrupting the operator. We have performed experiments in simple simulated robotic domains that illustrate the effectiveness of our approach.</italic> </p>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2024-11-15
DOI
https://doi.org/10.4271/2024-01-3080
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

SELECTIVE ACQUISITION OF OPERATOR KNOWLEDGE FOR SAFER SEMI-AUTONOMOUS ROBOT OPERATIONS

Michael Maxim, Edmund H. Durfee, David Karmol, Satinder Singh
SAE technical papers on CD-ROM/SAE technical paper series
Robot Manipulation and Learning
article

SELECTIVE ACQUISITION OF OPERATOR KNOWLEDGE FOR SAFER SEMI-AUTONOMOUS ROBOT OPERATIONS

Michael Maxim, Edmund H. Durfee, David Karmol, Satinder Singh
article en

Abstract

<title>ABSTRACT</title> <p> <italic>We have developed techniques for a robot to compute its expected myopic gain in performance from asking its operator specific questions, such as questions about how risky a particular movement action is around pedestrians. Coupled with a model of the operator’s costs for responding to inquiries, these techniques form the core of a new algorithm that iteratively allows the robot to decide what questions are in expectation most valuable to ask the operator and whether their value justifies potentially interrupting the operator. We have performed experiments in simple simulated robotic domains that illustrate the effectiveness of our approach.</italic> </p>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
University of Michigan (US)
Robot Manipulation and Learning
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