Emily Taylor

dblp:78/1584 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-0248-9545ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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 · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
trust in robots
1.012026
"Meet My Sidekick!": Effects of Separate Identities and Control of a Single Robot in HRI · HRI 2026
Human-robot interaction
collaborative task
0.312026
"Meet My Sidekick!": Effects of Separate Identities and Control of a Single Robot in HRI · HRI 2026

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

mixed-design study · 1.0
YearPublicationVenuePosition
2026 "Meet My Sidekick!": Effects of Separate Identities and Control of a Single Robot in HRI
abstract
The presentation of a robot's capability and identity directly influences a human collaborator's perception and implicit trust in the robot. Unlike humans, a physical robot can simultaneously present different identities and have them reside and control different parts of the robot. This paper presents a novel study that investigates how users perceive a robot where different robot control domains (head and gripper) are presented as independent robots. We conducted a mixed design study where participants experienced one of three presentations: a single robot, two agents with shared full control (co-embodiment), or two agents with split control across robot control domains (split-embodiment). Participants underwent three distinct tasks -- a mundane data entry task where the robot provides motivational support, an individual sorting task with isolated robot failures, and a collaborative arrangement task where the robot causes a failure that directly affects the human participant. Participants perceived the robot as residing in the different control domains and were able to associate robot failure with different identities. This work signals how future robots can leverage different embodiment configurations to obtain the benefit of multiple robots within a single body.
Drake Moore, Arushi Aggarwal, Emily Taylor, Sarah Zhang, Taskin Padir, Xiang Zhi Tan
HRI3