  Human-robot teams will soon be expected to accomplish complex tasks in
high-risk and uncertain environments. Here, the human may not necessarily be a
robotics expert, but will need to establish a baseline understanding of the
robot's abilities in order to appropriately utilize and rely on the robot. This
willingness to rely, also known as trust, is based partly on the human's belief
in the robot's proficiency at a given task. If trust is too high, the human may
push the robot beyond its capabilities. If trust is too low, the human may not
utilize it when they otherwise could have, wasting precious resources. In this
work, we develop and execute an online human-subjects study to investigate how
robot proficiency self-assessment reports based on Factorized Machine
Self-Confidence affect operator trust and task performance in a grid world
navigation task. Additionally we present and analyze a metric for trust level
assessment, which measures the allocation of control between an operator and
robot when the human teammate is free to switch between teleportation and
autonomous control. Our results show that an a priori robot self-assessment
report aligns operator trust with robot proficiency, and leads to performance
improvements and small increases in self-reported trust.
