  In human-robot collaboration (HRC), human trust in the robot is the human
expectation that a robot executes tasks with desired performance. A
higher-level trust increases the willingness of a human operator to assign
tasks, 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 fragile
and trust loss is triggered easily when robots show incapability of task
executions, making the trust maintenance challenging. To maintain human trust,
in this research, a trust repair framework is developed based on a
human-to-robot attention transfer (H2R-AT) model and a user trust study. The
rationale of this framework is that a prompt mistake correction restores human
trust. With H2R-AT, a robot localizes human verbal concerns and makes prompt
mistake corrections to avoid task failures in an early stage and to finally
improve human trust. User trust study measures trust status before and after
the behavior corrections to quantify the trust loss. Robot experiments were
designed to cover four typical mistakes, wrong action, wrong region, wrong
pose, and wrong spatial relation, validated the accuracy of H2R-AT in robot
behavior 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 mistake
correction accuracy and the trust improvement.
