Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866911402950656000 |
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| author | Campagna, Giulio Lagomarsino, Marta Lorenzini, Marta Chrysostomou, Dimitrios Rehm, Matthias Ajoudani, Arash |
| author_facet | Campagna, Giulio Lagomarsino, Marta Lorenzini, Marta Chrysostomou, Dimitrios Rehm, Matthias Ajoudani, Arash |
| contents | Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhancing trajectories based on operator feedback. This feedback serves as ground truth for training machine learning models to predict trust levels from behavioral indicators. The framework was tested in a chemical industry scenario where a robot assisted a human operator in mixing chemicals. Machine learning models classified trust with over 80\% accuracy, with the Voting Classifier achieving 84.07\% accuracy and an AUC-ROC score of 0.90. These findings underscore the effectiveness of data-driven methods in assessing trust within human-robot collaboration, emphasizing the valuable role behavioral indicators play in predicting the dynamics of human trust. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19856 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability Campagna, Giulio Lagomarsino, Marta Lorenzini, Marta Chrysostomou, Dimitrios Rehm, Matthias Ajoudani, Arash Robotics Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhancing trajectories based on operator feedback. This feedback serves as ground truth for training machine learning models to predict trust levels from behavioral indicators. The framework was tested in a chemical industry scenario where a robot assisted a human operator in mixing chemicals. Machine learning models classified trust with over 80\% accuracy, with the Voting Classifier achieving 84.07\% accuracy and an AUC-ROC score of 0.90. These findings underscore the effectiveness of data-driven methods in assessing trust within human-robot collaboration, emphasizing the valuable role behavioral indicators play in predicting the dynamics of human trust. |
| title | Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.19856 |