Assessing, Explaining, and Conveying Robot Proficiency for Human-Robot Teaming

Abstract

This workshop focuses on issues surrounding human-robot interaction for robot self-assessment of system proficiency. For example, how should a robot convey predicted ability on a new task? How should it report performance on a task that was just completed? Communities in both computer science and robotics have addressed questions of introspection to monitor system performance and adjust behavior to guarantee or improve performance. Self-assessment can range from simple detection of proficiency up through evaluation, explanation, and prediction. Robots need the ability to make assessments and communicate them a priori, in situ, and post hoc in order to support effective autonomy and utilization by human partners and supervisors. This is a pressing challenge for human-robot interaction for a variety of reasons. Prior work has shown that robot expression of performance can alter human perception of the robot and decisions on control allocation. There is also significant evidence in robotics that accurately setting human expectations is critical, especially when proficiency is below human expectations. Therefore, more knowledge is needed on how systems should communicate specifics about current and future task competence.