AUTHOR=Alhaji Basel , Prilla Michael , Rausch Andreas 

TITLE=Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.703504

DOI=10.3389/frai.2021.703504

ISSN=2624-8212

ABSTRACT=Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is an intensive research on human-robot trust, there is a lack of knowledge about the factors that affect trust in a synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement dynamics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn enables a dynamic and spontaneous cooperation.
Prior research suggests that trust toward a robot is affected by its reliability as well as its feedback to the user. Accordingly, and based on factors identified to shape human trust in robots, we conducted an experiment in a mixed-reality environment, where thirty-two participants collaborated with a virtual CoBot on disassembling electric car batteries in the context of recycling. The experiment has two main objectives with respect to trust dynamics; they are: (1) to explore and discover the (dynamics of) relevant trust factors during human-robot physical collaboration, and (2) to explore the impact of robot reliability and feedback on the accumulation and dissipation of human trust in robots.
Results highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might lead to trust which should be accounted for in trust measurement tools. Additionally, robot’s assurances accuracy has different effect on trust depending on the robot’s collaboration condition. This provides a hint to designers on when assurances are necessary and when they are redundant.