Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface
This addresses the problem of effective training for human-robot teams, which is incremental as it builds on existing after-action review methods by adapting them to include robotic teammates.
The study tackled the challenge of training human-robot teams by proposing a Virtual Spectator Interface (VSI) to enhance transparency through visual feedback in simulated search missions, finding that while it did not significantly improve team performance, it led to more improvement in situational awareness compared to other conditions.
After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies that compared different formats of AARs have mainly focused on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, designed a 1 x 3 between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects SA over the other conditions.