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Increasing user trust in a fetching robot using explainable AI in a traded control paradigm

Published: 30 June 2020 Publication History

Abstract

Recently, there has been an increase use of collaborative robots in manufacturing, healthcare, military, and personal use scenarios. Such robots operate under shared or traded control paradigms with their human operators or users. Therefore, it is important to understand how to address and improve issues of trust between the humans and collaborative robots. In this paper, we investigate the impact of robotic agent transparency to their subjective trust level by a human operator. Several experiments were conceived with the help of a fetching mobile robot under traded control, and data such as subjective trust level was collected during experimentation. Results indicate that trust is easier to lose than it is to gain. Furthermore, results also indicate that agent transparency's effect on operator trust is more significant in tasks of increasing complexity.

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  • (2024)Forging TrustUsing Real-Time Data and AI for Thrust Manufacturing10.4018/979-8-3693-2615-2.ch003(43-71)Online publication date: 10-May-2024

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    cover image ACM Other conferences
    PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
    June 2020
    574 pages
    ISBN:9781450377737
    DOI:10.1145/3389189
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • NSF: National Science Foundation
    • CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
    • NCRS: Demokritos National Center for Scientific Research

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    Published: 30 June 2020

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    1. explainable autonomy
    2. mobile manipulation

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    • (2024)Forging TrustUsing Real-Time Data and AI for Thrust Manufacturing10.4018/979-8-3693-2615-2.ch003(43-71)Online publication date: 10-May-2024

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