Multi-Task Trust Transfer for Human-Robot Interaction

Abstract

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.

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• Sequence to Sequence Learning with Neural Networks
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• To Lead or to Follow? Adaptive Robot Task Planning in Human-Robot Collaboration
• Trust, Shared Understanding and Locus of Control in Mixed-Initiative Robotic Systems
• Using Trust for Heterogeneous Human-Robot Team Task Allocation
• Embedding Symbolic Temporal Knowledge into Deep Sequential Models
• Repairing Human Trust by Promptly Correcting Robot Mistakes with An Attention Transfer Model
• Reverse Psychology in Trust-Aware Human-Robot Interaction