Modeling Supervisor Safe Sets for Improving Collaboration in Human-Robot Teams

TL;DR

This paper introduces a method to reduce false positives in human-robot collaboration by learning a supervisor’s safe set and guiding robot behavior, thereby optimizing attention and safety judgments.

Contribution

It presents a novel approach that learns the supervisor's safety model to improve robot safety interventions and reduces false positives in safety judgments.

Findings

Significant reduction in false positives ($p = 0.0328$) in user study.

Proven theoretical reduction of false positives with the proposed method.

Enhanced safety and efficiency in human-robot teams.

Abstract

When a human supervisor collaborates with a team of robots, their attention is divided and cognitive resources are at a premium. We aim to optimize the distribution of these resources and the flow of attention. To this end, we propose the model of an idealized supervisor to describe human behavior. Such a supervisor employs a potentially inaccurate internal model of the the robots' dynamics to judge safety. We represent these safety judgements by constructing a safe set from this internal model using reachability theory. When a robot leaves this safe set, the idealized supervisor will intervene to assist, regardless of whether or not the robot remains objectively safe. False positives, where a human supervisor incorrectly judges a robot to be in danger, needlessly consume supervisor attention. In this work, we propose a method that decreases false positives by learning the supervisor's safe set and using that information to govern robot behavior. We prove that robots behaving according to our approach will reduce the occurrence of false positives for our idealized supervisor model. Furthermore, we empirically validate our approach with a user study that demonstrates a significant ($p = 0.0328$) reduction in false positives for our method compared to a baseline safety controller.