Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. ‘Not So Real’ Robots

This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}=34$</tex> studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974086
Citations
12
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
29.77
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article

Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. ‘Not So Real’ Robots

Rui Guan, Connor Esterwood, Xin Ye, Lionel Robert
12 citations
Reinforcement Learning in Robotics
29.77
article

Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. ‘Not So Real’ Robots

Rui Guan, Connor Esterwood, Xin Ye, Lionel Robert
article en
12 citations

Abstract

This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}=34$</tex> studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.

University of Michigan (US)
Openalex Percentile: Top 0%
Reinforcement Learning in Robotics
29.77
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