Development of A Multimodal Trust Database in Human-Robot Collaborative Contexts

Robots are gradually being incorporated into the workforce to assist with labor-intensive and repetitive tasks, especially in smart manufacturing contexts. This leads to increased human-robot collaboration, which may be an unfamiliar, distrustful, and uncomfortable situation for inexperienced people to navigate. Motivated by these issues and aiming to have a comprehensive understanding of the factors that affect people’s trust in robots, we developed a new trust database by investigating the trust between human collaborators wearing four biological sensors and a robot performing collaborative tasks. Using these sensors, we collected trust-related physiological human factors from the brain (EEG), heart (ECG), forearm (EMG), and eyes during human-robot collaborative tasks. As well as a trust rating through a questionnaire, this allows for the creation of a multimodal human-robot trust database (TrustBase). TrustBase provides insightful guidance to optimize and improve the environment deployment and robot configuration in human-robot partnerships within smart manufacturing contexts.