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

摘要

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.