The Transfer of Human Trust in Robot Capabilities across Tasks

Abstract

Trust is crucial in shaping human interactions with one another and with robots. This work investigates how human trust in robot capabilities transfers across tasks. We present a human-subjects study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. Our findings lead to a functional view of trust and two novel predictive models-a recurrent neural network architecture and a Bayesian Gaussian process-that capture trust evolution and transfer via latent task representations. Experiments show that the two proposed models outperform existing approaches when predicting trust across unseen tasks and participants. These results indicate that (i) a task-dependent functional trust model captures human trust in robot capabilities more accurately, and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-based robot decision-making for fluent human-robot interaction. In particular, our models can be used to derive robot policies that mitigate under-trust or over-trust by human teammates in collaborative multi-task settings.