Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913790437621760 |
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| author | Dallas, Sean Qiang, Hongjiao AbuHijleh, Motaz Jo, Wonse Riegner, Kayla Smereka, Jon Robert, Lionel Louie, Wing-Yue Tilbury, Dawn M. |
| author_facet | Dallas, Sean Qiang, Hongjiao AbuHijleh, Motaz Jo, Wonse Riegner, Kayla Smereka, Jon Robert, Lionel Louie, Wing-Yue Tilbury, Dawn M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface Dallas, Sean Qiang, Hongjiao AbuHijleh, Motaz Jo, Wonse Riegner, Kayla Smereka, Jon Robert, Lionel Louie, Wing-Yue Tilbury, Dawn M. Human-Computer Interaction Artificial Intelligence Robotics H.5.2; I.2.9 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. |
| title | Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface |
| topic | Human-Computer Interaction Artificial Intelligence Robotics H.5.2; I.2.9 |
| url | https://arxiv.org/abs/2503.09849 |