Trust is crucial for technological acceptance, continued usage, and teamwork. However, human-robot trust, and human-machine trust more generally, suffer from terminological disagreement and construct proliferation. By comparing, mapping, and analyzing well-constructed trust survey instruments, this work uncovers a consensus structure of trust in human–machine interaction. To do so, we identify the most frequently cited and best-validated human-machine and human-robot trust questionnaires as well as the best-established factors that form the dimensions and antecedents of such trust. To reduce both confusion and construct proliferation, we provide a detailed mapping of terminology between questionnaires. Furthermore, we perform a meta-analysis of the regression models which emerged from the experiments that employed multi-factorial survey instruments. Based on this meta-analysis, we provide the most complete, experimentally validated model of human-machine and human-robot trust to date. This convergent model establishes an integrated framework for future research. It determines the current boundaries of trust measurement and where further investigation and validation are necessary. We close by discussing how to choose an appropriate trust survey instrument and how to design for trust. By identifying the internal workings of trust, a more complete basis for measuring trust is developed that is widely applicable.

Effective human-aware robots should anticipate their user’s intentions. During hand-eye coordination tasks, gaze often precedes hand motion and can serve as a powerful predictor for intent. However, cooperative tasks where a semi-autonomous robot serves as an extension of the human hand have rarely been studied in the context of hand-eye coordination. We hypothesize that accounting for anticipatory eye movements in addition to the movements of the robot will improve intent estimation. This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks. We found that with proper feature selection, accuracies exceeding 94% and AUC greater than 91% are achievable with several classification algorithms but that anticipatory gaze data did not improve intent prediction.