A Novel Computational Framework of Robot Trust for Human-Robot Teams

When humans collaborate, they form positive or negative experiences with each other. These experiences depend on various factors such as the individual's skills, abilities, and agency. In this paper, we consider human-robot collaborations and present a novel model of an autonomous robot's trust in humans based on the probability of the robot having a positive experience with the human. The model defines a dynamic trust-building process that translates into a computationallyaccessible implementation. We hypothesize predictors of a positive experience with human teammates and derive trust in individual humans. As the interactions continue, team members develop an affinity toward each other. The robot's affinity towards humans can be viewed as kinship, and we also investigate how kinship affects trust and distrust. We present an algorithm for how the robot may use kinship-mediated trust in its decision-making, and demonstrate its use in simulated missions truly requiring human-robot collaboration.