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Aidan Allchin

dblp:215/6636 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—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

TopicWeightPapersLastEvidence papers
Human-robot interaction
social robot
0.312018
Characterizing the Design Space of Rendered Robot Faces · HRI 2018
Human-robot interaction › robot design
robot face design
0.112018
Characterizing the Design Space of Rendered Robot Faces · HRI 2018
Human-robot interaction › robot perception
robot face perception
0.112018
Characterizing the Design Space of Rendered Robot Faces · HRI 2018

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

survey · 0.3coding · 0.3
YearPublicationVenuePosition
2018 Characterizing the Design Space of Rendered Robot Faces
abstract
Faces are critical in establishing the agency of social robots; however, building expressive mechanical faces is costly and difficult. Instead, many robots built in recent years have faces that are rendered onto a screen. This gives great flexibility in what a robot's face can be and opens up a new design space with which to establish a robot's character and perceived properties. Despite the prevalence of robots with rendered faces, there are no systematic explorations of this design space. Our work aims to fill that gap. We conducted a survey and identified 157 robots with rendered faces and coded them in terms of 76 properties. We present statistics, common patterns, and observations about this data set of faces. Next, we conducted two surveys to understand people's perceptions of rendered robot faces and identify the impact of different face features. Survey results indicate preferences for varying levels of realism and detail in robot faces based on context, and indicate how the presence or absence of specific features affects perception of the face and the types of jobs the face would be appropriate for.
Alisa Kalegina, Grace Schroeder, Aidan Allchin, Keara Berlin, Maya Cakmak
HRI3