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Marta Lagomarsino

6 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

Information-Theoretic Detection of Bimanual Interactions for Dual-Arm Robot Plan Generation

Elena Merlo, Marta Lagomarsino, Arash Ajoudani
RA-L 2025

Programming by demonstration is a strategy to simplify the robot programming process for non-experts via human demonstrations. However, its adoption for bimanual tasks is an underexplored problem due to the complexity of hand coordination, which also hinders data recording. This letter presents a no

Cited by 6Source ↗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 ↗
2023

Maximising Coefficiency of Human-Robot Handovers Through Reinforcement Learning

Marta Lagomarsino, Marta Lorenzini, Merryn Dale Constable, Elena De Momi, Cristina Becchio, Arash Ajoudani
RA-L 2023

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may

Cited by 19Source ↗Scholar ↗
2022

Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Tasks

Marta Lagomarsino, Marta Lorenzini, Elena De Momi, Arash Ajoudani
IROS 2022poster

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the…

Cited by 29Source ↗Scholar ↗