Demo Data from EDBT 2026 / reviewers in the wild / expert
Samuel Brimhall
dblp:304/4202
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—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 · 87% Usability and user experience research · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › robot design
robot sound design |
0.5 | 1 | 2021 | Bringing WALL-E out of the Silver Screen: Understanding How Transformative Robot Sound Affects Human Perception · ICRA 2021 |
Human-robot interaction › human behavior modeling › social perception
social perception of robots |
0.5 | 1 | 2021 | Bringing WALL-E out of the Silver Screen: Understanding How Transformative Robot Sound Affects Human Perception · ICRA 2021 |
Usability and user experience research › evaluation methodology
subjective evaluation |
0.1 | 1 | 2021 | Bringing WALL-E out of the Silver Screen: Understanding How Transformative Robot Sound Affects Human Perception · ICRA 2021 |
Methods — techniques the papers use, named apart from their topics
online video-based survey · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Bringing WALL-E out of the Silver Screen: Understanding How Transformative Robot Sound Affects Human PerceptionabstractLovable robots in movies regularly beep, chirp, and whirr, yet robots in the real world rarely deploy such sounds. Despite preliminary work supporting the perceptual and objective benefits of intentionally-produced robot sound, relatively little research is ongoing in this area. In this paper, we systematically evaluate transformative robot sound across multiple robot archetypes and behaviors. We conducted a series of five online video-based surveys, each with N≈ 100 participants, to better understand the effects of musician-designed transformative sounds on perceptions of personal, service, and industrial robots. Participants rated robot videos with transformative sound as significantly happier, warmer, and more competent in all five studies, as more energetic in four studies, and as less discomforting in one study. Overall, results confirmed that transformative sounds consistently improve subjective ratings but may convey affect contrary to the intent of affective robot behaviors. In future work, we will investigate the repeatability of these results through in-person studies and develop methods to automatically generate transformative robot sound. This work may benefit researchers and designers who aim to make robots more favorable to human users. Brian J. Zhang, Nick Stargu, Samuel Brimhall, Lilian Chan, Jason Fick, Naomi T. Fitter |
ICRA | 3 |