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Hongjiao Qiang

dblp:393/4165 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 77% Collaborative and social computing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
human-robot collaboration
0.912025
Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface · ICRA 2025
Collaborative and social computing
team training
0.312025
Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface · ICRA 2025

Methods — techniques the papers use, named apart from their topics

between-subjects experiment · 0.9
YearPublicationVenuePosition
2025 Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface
abstract
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 comparing different formats of AARs have focused mainly 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, and designed a 1$\times 3$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.
Sean Dallas, Hongjiao Qiang, Motaz AbuHijleh, Wonse Jo, Kayla Riegner, Jonathon M. Smereka, Lionel P. Robert Jr., Wing-Yue Geoffrey Louie, Dawn M. Tilbury
ICRA2