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This work contributes both experimental findings and novel computational human-robot trust models for multi-task settings. We describe Bayesian non-parametric and neural models, and compare their performance on data collected from realworld human-subjects study. Our study spans two distinct task domains: household tasks performed by a Fetch robot, and a virtual reality driving simulation of an autonomous vehicle performing a variety of maneuvers. We find that human trust changes and transfers across tasks in a structured manner based on perceived task characteristics. Our results suggest that task-dependent functional trust models capture human trust in robot capabilities more accurately, and trust transfer across tasks can be inferred to a good degree. We believe these models are key for enabling trust-based robot decisionmaking for natural human-robot interaction.

Trust Dynamics and Transfer across Human-Robot Interaction Tasks: Bayesian and Neural Computational Models