Who is Supporting Whom? Balanced and Unbalanced Support Perceptions Shaping Acceptance of Collaborative Robots

Humans and industrial collaborative robots (cobots) form intricate relationships as they both possess agency, share spaces, and must coordinate effectively to work together. In initial interactions, much like human-team dynamics, the perceived contribution of each agent plays a crucial role in shaping the collaborative experience and the acceptance of cobots. Beyond unilateral support perceptions from or to the cobot—termed as Received Support (RS) and Given Support (GS)—Mutual Support (MS) embodies a balanced, reciprocal dynamic where humans view their contribution as equal to that of the cobot. Drawing from social knowledge in human teams and aligning with recent interpretations of human-robot interactions, we propose that perceiving MS can foster fluent, trusting, and harmonious collaboration, ultimately increasing cobot acceptance in work routines. In this observational study, 56 novices collaborated with the industrial cobot UR10e to assess support dynamics. We explored how perceptions of balanced (MS) or unbalanced support (RS and GS) impact task performance (i.e., task time), Cognitive Factors (COG-F), User Experience (UX-F), and Human-Robot Relationship Factors (HRR-F), ultimately influencing cobot acceptance. We also examined the influence of initial attitudes toward robots. The findings suggest that fostering perceptions of MS, rather than focusing solely on functionality or work relief (RS), can lead to smoother integration and greater acceptance of cobots, especially among novices, regardless of their initial attitudes toward robots.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10973856
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48

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article

Who is Supporting Whom? Balanced and Unbalanced Support Perceptions Shaping Acceptance of Collaborative Robots

Giulia Buodo, Federica Nenna, Egle Maria Orlando, Luciano Gamberini et al.
1 citations
AI in Service Interactions
2.48
article

Who is Supporting Whom? Balanced and Unbalanced Support Perceptions Shaping Acceptance of Collaborative Robots

Giulia Buodo, Federica Nenna, Egle Maria Orlando, Luciano Gamberini, Davide Zanardi
article en
1 citations

Abstract

Humans and industrial collaborative robots (cobots) form intricate relationships as they both possess agency, share spaces, and must coordinate effectively to work together. In initial interactions, much like human-team dynamics, the perceived contribution of each agent plays a crucial role in shaping the collaborative experience and the acceptance of cobots. Beyond unilateral support perceptions from or to the cobot—termed as Received Support (RS) and Given Support (GS)—Mutual Support (MS) embodies a balanced, reciprocal dynamic where humans view their contribution as equal to that of the cobot. Drawing from social knowledge in human teams and aligning with recent interpretations of human-robot interactions, we propose that perceiving MS can foster fluent, trusting, and harmonious collaboration, ultimately increasing cobot acceptance in work routines. In this observational study, 56 novices collaborated with the industrial cobot UR10e to assess support dynamics. We explored how perceptions of balanced (MS) or unbalanced support (RS and GS) impact task performance (i.e., task time), Cognitive Factors (COG-F), User Experience (UX-F), and Human-Robot Relationship Factors (HRR-F), ultimately influencing cobot acceptance. We also examined the influence of initial attitudes toward robots. The findings suggest that fostering perceptions of MS, rather than focusing solely on functionality or work relief (RS), can lead to smoother integration and greater acceptance of cobots, especially among novices, regardless of their initial attitudes toward robots.

University of Padua (IT)
European Commission
Openalex Percentile: Top 11%
AI in Service Interactions
2.48
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Who is Supporting Whom? Balanced and Unbalanced Support Perceptions Shaping Acceptance of Collaborative Robots — Giulia Buodo, Federica Nenna, et al. · (2025) | TGRS Research Map | TGRS