With a Little Help from My Cobot – User-centered Evaluation of Different Human-Cobot Collaboration Types Exemplified in Textile Composite Manufacturing

The use of collaborative robots (cobots) in Industry 5.0, where humans and robots interact in open workspaces, offers advantages in terms of physical support for humans and improves productivity, flexibility, and accuracy. However, these benefits can only be realised if human–robot collaboration (HRC) is carefully aligned with human needs and designed as effective teamwork rather than mere technical assistance. The production of textile-based composite parts requires many manual steps due to the process's complexity and the precision needed. Introducing a (partially) automated process using cobots demands special attention. To explore this, we conducted an empirical user study (n = 30) using the production of a propeller blade made from carbon-fibre reinforced plastics, comparing three collaboration conditions ( sequential , simultaneous , supportive ) against a manual baseline. Evaluations of technical performance (completion time, draping quality) and well-being parameters (psychological need satisfaction, perceived safety, perceived effort) revealed that simultaneous collaboration provided the most balanced overall trade-off between quality and user well-being. While the manual condition was fastest and received high ratings for psychological need satisfaction, it did not outperform cobot-assisted conditions in quality. Surprisingly, the supportive condition, despite being the most collaborative in theory, performed least favorably in both technical and well-being parameters. To situate these findings within a broader adoption perspective, overall acceptance of cobot support was assessed as a contextual attitudinal evaluation. An exploratory acceptance-related assessment based on UTAUT2-related constructs provided preliminary indications that effort expectancy, hedonic motivation, performance expectancy, and trust may be relevant factors shaping attitudes toward cobot-supported production. Overall, the study provides design-relevant insights for implementing HRC in skilled manual production, highlighting that effective cobot assistance depends on aligning collaboration design with task complexity, user autonomy, and ease of use rather than maximising technical integration.

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Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2026-10-03
DOI
https://doi.org/10.1145/3850150
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

With a Little Help from My Cobot – User-centered Evaluation of Different Human-Cobot Collaboration Types Exemplified in Textile Composite Manufacturing

Luisa Vervier, Thomas Gries, Martina Ziefle, Hannah Dammers et al.
ACM Transactions on Human-Robot Interaction
Robot Manipulation and Learning
article

With a Little Help from My Cobot – User-centered Evaluation of Different Human-Cobot Collaboration Types Exemplified in Textile Composite Manufacturing

Luisa Vervier, Thomas Gries, Martina Ziefle, Hannah Dammers, Felix Glawe, Philipp Brauner
article en

Abstract

The use of collaborative robots (cobots) in Industry 5.0, where humans and robots interact in open workspaces, offers advantages in terms of physical support for humans and improves productivity, flexibility, and accuracy. However, these benefits can only be realised if human–robot collaboration (HRC) is carefully aligned with human needs and designed as effective teamwork rather than mere technical assistance. The production of textile-based composite parts requires many manual steps due to the process's complexity and the precision needed. Introducing a (partially) automated process using cobots demands special attention. To explore this, we conducted an empirical user study (n = 30) using the production of a propeller blade made from carbon-fibre reinforced plastics, comparing three collaboration conditions ( sequential , simultaneous , supportive ) against a manual baseline. Evaluations of technical performance (completion time, draping quality) and well-being parameters (psychological need satisfaction, perceived safety, perceived effort) revealed that simultaneous collaboration provided the most balanced overall trade-off between quality and user well-being. While the manual condition was fastest and received high ratings for psychological need satisfaction, it did not outperform cobot-assisted conditions in quality. Surprisingly, the supportive condition, despite being the most collaborative in theory, performed least favorably in both technical and well-being parameters. To situate these findings within a broader adoption perspective, overall acceptance of cobot support was assessed as a contextual attitudinal evaluation. An exploratory acceptance-related assessment based on UTAUT2-related constructs provided preliminary indications that effort expectancy, hedonic motivation, performance expectancy, and trust may be relevant factors shaping attitudes toward cobot-supported production. Overall, the study provides design-relevant insights for implementing HRC in skilled manual production, highlighting that effective cobot assistance depends on aligning collaboration design with task complexity, user autonomy, and ease of use rather than maximising technical integration.

ACM Transactions on Human-Robot Interaction
RWTH Aachen University (DE)
Openalex Percentile: Top 16%
Robot Manipulation and Learning
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