Demo Data from SIGIR 2026 / reviewers in the wild / expert
Henry Bard
dblp:177/8649
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
human-robot collaboration |
0.2 | 1 | 2016 | Natural Human-Robot Interaction Using Social Cues · HRI 2016 |
Human-robot interaction
social cues |
0.2 | 1 | 2016 | Natural Human-Robot Interaction Using Social Cues · HRI 2016 |
Human-robot interaction › robot control
pick-and-place task |
0.1 | 1 | 2016 | Natural Human-Robot Interaction Using Social Cues · HRI 2016 |
Methods — techniques the papers use, named apart from their topics
user study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Natural Human-Robot Interaction Using Social CuesabstractThis paper investigates the problem of how humans understand and control human-robot collaborative action and how to build natural interactions during human-robot collaborative action. We use a `pick and place' experiment to study collaborative activities between a human and a robot. The results show that even if human participants had a good understanding of the maximum reachability of the robot, they consistently take a surprisingly long time to help and assist the robot when a target object is out of its reach. We implemented a number of social cues in the experiment, analysed their effects in order to identify the role they could play to improve the fluency of human-robot collaboration. The experimental results showed that when the robot uses head movements, two hands or a gesture to indicate non-reachability, people react in a more natural way to assist the robot. Hugo Romat, Mary-Anne Williams, Xun Wang 0006, Benjamin Johnston, Henry Bard |
HRI | 5 |