Robot head movements and human effort in the evaluation of tracking performance

Abstract

People detection and tracking are essential capabilities in human-robot interaction (HRI). Typically, a tracker performance is evaluated by measuring objective data, such as the tracking error. However, in HRI applications, human- tracking performance does not have to be evaluated by considering it as a passive sensing behavior, but as an active sensing process, where both the robot and the human are involved within-the-loop. In this context, we foresee that the robotic non-verbal feedback, such as the head movement, plays an important role in improving the system tracking performance, as well as in reducing the human effort in the interactive tracking process. In order to verify this assumption, we evaluate a tracker performance in a joint task between a human and a robot, modeled as a game, and in three different settings. We adopt common HRI performance measures, such as the robot attention demand or the human effort, to evaluate the HRI human tracking performance scaling up with respect to the used robot feedback channels.