Markkula, G and Dogar, M orcid.org/0000-0002-6896-5461 (2024) Models of Human Behavior for Human-Robot Interaction and Automated Driving: How Accurate Do the Models of Human Behavior Need to Be? IEEE Robotics and Automation Magazine, 31 (3). 115 -120. ISSN 1070-9932
Abstract
There are many examples of cases where access to improved models of human behavior and cognition has allowed the creation of robots that can better interact with humans, and not least in road vehicle automation, this is a rapidly growing area of research. Human–robot interaction (HRI) therefore provides an important applied setting for human behavior modeling—but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: a successful HRI, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasize that identifying what those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter in cases where robots are to be involved in interactions that currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI and how to mitigate against those risks.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S005056/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Jun 2022 15:52 |
Last Modified: | 07 Nov 2024 15:47 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MRA.2022.3182892 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187556 |