Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability

• URL: http://arxiv.org/abs/2601.19856v1
• Date: Tue, 27 Jan 2026 18:12:44 GMT
• Title: Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability
• Abstract summary: This study introduces a data-driven framework to assess trust using behavioral indicators.<n>The framework was tested in a chemical industry scenario where a robot assisted a human operator in mixing chemicals.<n>Machine learning models classified trust with over 80% accuracy, with the Voting algorithm achieving 84.07% accuracy and an AUCROC score of 0.90.
• Score: 17.91172666743091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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