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In human-robot collaboration (HRC), human trust in the robot is the humanexpectation that a robot executes tasks with desired performance. Ahigher-level trust increases the willingness of a human operator to assigntasks, share plans, and reduce the interruption during robot executions,thereby facilitating human-robot integration both physically and mentally.However, due to real-world disturbances, robots inevitably make mistakes,decreasing human trust and further influencing collaboration. Trust is fragileand trust loss is triggered easily when robots show incapability of taskexecutions, making the trust maintenance challenging. To maintain human trust,in this research, a trust repair framework is developed based on ahuman-to-robot attention transfer (H2R-AT) model and a user trust study. Therationale of this framework is that a prompt mistake correction restores humantrust. With H2R-AT, a robot localizes human verbal concerns and makes promptmistake corrections to avoid task failures in an early stage and to finallyimprove human trust. User trust study measures trust status before and afterthe behavior corrections to quantify the trust loss. Robot experiments weredesigned to cover four typical mistakes, wrong action, wrong region, wrongpose, and wrong spatial relation, validated the accuracy of H2R-AT in robotbehavior corrections; a user trust study with $252$ participants was conducted,and the changes in trust levels before and after corrections were evaluated.The effectiveness of the human trust repairing was evaluated by the mistakecorrection accuracy and the trust improvement.

Repairing Human Trust by Promptly Correcting Robot Mistakes with An Attention Transfer Model