Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Alena M. Hutchinson

dblp:237/8750 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 · 87% Usability and user experience research · 13%
Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › social navigation
crowd navigation
0.412019
Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019
Human-robot interaction › robot navigation
human-aware navigation
0.412019
Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019
Human-robot interaction › robot navigation
social robot navigation
0.412019
Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019
Usability and user experience research
user experience evaluation
0.112019
Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019

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

within-subjects study · 0.8trajectory analysis · 0.8statistical analysis · 0.8
YearPublicationVenuePosition
2019 Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment
abstract
State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence regarding the user experience and the robot behavior; field studies may suffer from the noise imposed by uncontrollable factors from the environment; controlled lab experiments often fail to properly enforce challenging interaction settings. This paper contributes a first step towards addressing the outlined gaps in the literature. We present an original experiment, designed to test the implicit interaction between a mobile robot and a group of navigating human participants, under challenging settings in a controlled lab environment. We conducted a large-scale, within-subjects design study with 105 participants, exposed to three different conditions, corresponding to three distinct navigation strategies, executed by a telepresence robot (two autonomous, one teleoperated). We analyzed observed human and robot trajectories, under close interaction settings and participants' impressions regarding the robot's behavior. Key findings, extracted from a comparative statistical analysis include: (1) evidence that human acceleration is lower when navigating around an autonomous robot compared to a teleoperated one; (2) the lack of evidence to support the conventional expectation that teleoperation would be humans' preferred strategy. To the best of our knowledge, our study is unique in terms of goals, settings, thoroughness of evaluation and sample size.
Christoforos I. Mavrogiannis, Alena M. Hutchinson, John Macdonald, Patrícia Alves-Oliveira, Ross A. Knepper
HRI2