  Trust is essential in shaping human interactions with one another and with
robots. This paper discusses how human trust in robot capabilities transfers
across multiple tasks. We first present a human-subject 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.
The findings expand our understanding of trust and inspire new differentiable
models of trust evolution and transfer via latent task representations: (i) a
rational Bayes model, (ii) a data-driven neural network model, and (iii) a
hybrid model that combines the two. Experiments show that the proposed models
outperform prevailing models when predicting trust over unseen tasks and users.
These results suggest that (i) task-dependent functional trust models capture
human trust in robot capabilities more accurately, and (ii) trust transfer
across tasks can be inferred to a good degree. The latter enables
trust-mediated robot decision-making for fluent human-robot interaction in
multi-task settings.
