False alarm metrics: Evaluating safety in human robot interactions

Human robot teams combining the complementary capabilities of robots and humans towards solving potentially complex service tasks are gaining wide spread popularity. Many of these tasks will involve close interactions between the robot and the human it serves thereby making safety a crucial parameter. Erroneous interaction that inevitably arises between human and the robot causes accidents in service robotic applications. Currently, there are no metrics available in human robot interaction community for analyzing erroneous interactions. In this paper, we put forward a new class of false alarm metrics to define, classify and quantify the effects of erroneous interactions in human robot teams and explore the relationship between false alarms, and safety in service robots. We extend the receiver operating characteristics (ROC) curve commonly used in signal processing community to classify robots based on their associated risks. We also show the utility of the designed false alarm metrics and extended ROC curve by applying them to a service robot, Robo-Erectus@Home across tele-operation and semi-autonomous modes of autonomy.