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Giulio Campagna

4 accepted papers

2026

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

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
ICRA 2026poster

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-enhanci…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

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

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
RA-L 2025

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-enhanci

Cited by 3Source ↗Scholar ↗
2025

Fostering Trust Through Gesture and Voice-Controlled Robot Trajectories in Industrial Human-Robot Collaboration

Giulio Campagna, Christoph Frommel, Tobias Haase, Alberto Gottardi, Enrico Villagrossi, Dimitrios Chrysostomou +1
ICRA 2025

In the Industry 5.0 era, the focus shifts from basic automation to fostering collaboration between humans and robots. Trust is crucial in this new paradigm, enabling smooth interaction, especially for users with limited robotics knowledge. This study presents a novel framework that uses human hand g

Cited by 3Source ↗Scholar ↗
2024

Promoting Trust in Industrial Human-Robot Collaboration Through Preference-Based Optimization

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
RA-L 2024

This letter proposes a novel theoretical framework for promoting trust in human-robot collaboration (HRC). The framework exploits Preference-Based Optimization (PBO) and focuses on three key interaction parameters: robot velocity profile, human-robot separation distance, and vertical proximity to th

Cited by 23Source ↗Scholar ↗