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Aaron St. Clair

dblp:48/10890 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 2 first-authorHuman-computer interaction and ubiquitous computing · 3 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 1 first-author

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
Collaborative and social computing · 50% Human-robot interaction · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing
team performance
0.212015
How Robot Verbal Feedback Can Improve Team Performance in Human-Robot Task Collaborations · HRI 2015

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

user study · 0.2
YearPublicationVenuePosition
2015 How Robot Verbal Feedback Can Improve Team Performance in Human-Robot Task Collaborations
abstract
We detail an approach to planning effective verbal feedback during pairwise human-robot task collaboration. The approach is motivated by social science literature as well as existing work in robotics and is applicable to a variety of task scenarios. It consists of a dynamic, synthetic task implemented in an augmented reality environment. The result is combined robot task control and speech production, allowing the robot to actively participate and communicate with its teammate. A user study was conducted to experimentally validate the efficacy of the approach on a task in which a single user collaborates with an autonomous robot. The results demonstrate that the approach is capable of improving both objective measures of team performance and the user's subjective evaluation of both the task and the robot as a teammate.
Aaron St. Clair, Maja J. Mataric
HRI1
2011 Investigating the effects of visual saliency on deictic gesture production by a humanoid robot
abstract
In many collocated human-robot interaction scenarios, robots are required to accurately and unambiguously indicate an object or point of interest in the environment. Realistic, cluttered environments containing many visually salient targets can present a challenge for the observer of such pointing behavior. In this paper, we describe an experiment and results detailing the effects of visual saliency and pointing modality on human perceptual accuracy of a robot's deictic gestures (head and arm pointing) and compare the results to the perception of human pointing.
Aaron St. Clair, Ross Mead, Maja J. Mataric
RO-MAN1
2010 An architecture for rehabilitation task practice in socially assistive human-robot interaction
abstract
New approaches to rehabilitation and health care have developed due to advances in technology and human robot interaction (HRI). Socially assistive robotics (SAR) is a subcategory of HRI that focuses on providing assistance through hands-off interactions. We have developed a SAR architecture that facilitates multiple task-oriented interactions between a user and a robot agent. The architecture accommodates a variety of inputs, tasks, and interaction modalities that are used to provide relevant, real-time feedback to the participant. We have implemented the architecture and validated its technological feasibility in a small pilot study in which a SAR agent led three post-stroke individuals through an exercise scenario. In the following, we present our architecture design, and the results of the feasibility study.
Ross Mead, Eric Wade, Pierre Johnson, Aaron St. Clair, Shuya Chen, Maja J. Mataric
RO-MAN4