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

doi:10.1109/LRA.2024.3455792

Paper

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

Published Nov 1, 2024 · Giulio Campagna, Marta Lagomarsino, M. Lorenzini

IEEE Robotics and Automation Letters

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Abstract

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 the user's head. By iteratively refining these parameters based on qualitative feedback from human collaborators, the system dynamically adapts robot trajectories. This personalization aims to enhance users' confidence in the robot's actions and foster a more trusting collaborative environment. In our user study with fourteen participants, we simulated a chemical industrial scenario for the HRC task. Results suggest that the framework effectively promotes human operator confidence in the robot assistant, particularly for individuals with limited prior experience in robotics.

Study Snapshot

The PBO framework effectively promotes human operator confidence in robot assistants, particularly for those with limited prior robotics experience.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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

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