Computational models of artificial and natural trust in robotics: A systematic review and operational guide.

Trust forms the bedrock of successful human interactions, and its integration into human–robot collaboration remains a critical challenge. Contemporary research predominantly explores human trust in robotic systems, focusing on refining the appearance and behavior of artificial agents to foster their acceptability in social settings. However, this systematic review centers on the less-explored dimension of trust mechanisms within autonomous robotic systems. Our aim is to survey what we define as Computational Trust models, which encompass a robot’s capability to both assess the trustworthiness of other agents (“Artificial Trust”) and to predict their levels of trust towards itself (“Natural Trust”). To achieve this objective, an initial set of 1916 papers, ranging from 2013 to 2023, was collected from IEEE Xplore, Scopus, and ISI Web of Science. Eligibility criteria were then applied to this set to select works that designed a Computational Trust model for a robotics application, which was validated through an experiment. These criteria were agreed upon by all authors to ensure unanimous decisions on whether to retain or remove results. At the end of this process, 101 key papers were identified. Following the selection process, we conducted thorough analyses to cluster these works based on the type of Computational Trust model used, the application domain, the robotic platforms employed in the validation, the experimental design, and the evaluation metrics. Finally, we identify common trends in this emerging branch of Human–Robot Interaction and provide guidelines for scholars wishing to contribute to this field.