University of Hertfordshire

By the same authors

Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction. / Scheunemann, Marcus M.; Cuijpers, Raymond H.; Salge, Christoph.

29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020. p. 1340-1347.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Scheunemann, MM, Cuijpers, RH & Salge, C 2020, Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction. in 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1340-1347. https://doi.org/10.1109/RO-MAN47096.2020.9223478

APA

Scheunemann, M. M., Cuijpers, R. H., & Salge, C. (2020). Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction. In 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 1340-1347). IEEE. https://doi.org/10.1109/RO-MAN47096.2020.9223478

Vancouver

Scheunemann MM, Cuijpers RH, Salge C. Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction. In 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE. 2020. p. 1340-1347 https://doi.org/10.1109/RO-MAN47096.2020.9223478

Author

Scheunemann, Marcus M. ; Cuijpers, Raymond H. ; Salge, Christoph. / Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction. 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020. pp. 1340-1347

Bibtex

@inproceedings{5aa33b606c564e8aab5d2ba4ae76b63f,
title = "Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction",
abstract = "A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.",
author = "Scheunemann, {Marcus M.} and Cuijpers, {Raymond H.} and Christoph Salge",
note = "{\textcopyright} 2020 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.",
year = "2020",
month = oct,
day = "14",
doi = "10.1109/RO-MAN47096.2020.9223478",
language = "English",
isbn = "9781728160764",
pages = "1340--1347",
booktitle = "29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction

AU - Scheunemann, Marcus M.

AU - Cuijpers, Raymond H.

AU - Salge, Christoph

N1 - © 2020 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.

PY - 2020/10/14

Y1 - 2020/10/14

N2 - A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.

AB - A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.

U2 - 10.1109/RO-MAN47096.2020.9223478

DO - 10.1109/RO-MAN47096.2020.9223478

M3 - Conference contribution

SN - 9781728160764

SP - 1340

EP - 1347

BT - 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

PB - IEEE

ER -