Demo Data from VLDB 2026 / reviewers in the wild / expert
Benjamin A. Blumer
dblp:142/3021
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1
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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › nonverbal communication
gaze cue |
0.2 | 1 | 2014 | Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing · HRI 2014 |
Human-robot interaction
nonverbal communication |
0.2 | 1 | 2014 | Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing · HRI 2014 |
Human-robot interaction › physical human-robot interaction
object handover |
0.2 | 1 | 2014 | Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing · HRI 2014 |
Human-robot interaction › nonverbal communication
joint attention |
0.1 | 1 | 2014 | Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing · HRI 2014 |
Methods — techniques the papers use, named apart from their topics
user study · 0.2gaze behavior implementation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Meet me where i'm gazing: how shared attention gaze affects human-robot handover timingabstractIn this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naïve subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turn-taking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent. AJung Moon, Daniel Troniak, Brian T. Gleeson, Matthew K. X. J. Pan, Minhua Zheng, Benjamin A. Blumer, Karon E. MacLean, Elizabeth A. Croft |
HRI | 6 |