Demo Data from EDBT 2026 / reviewers in the wild / expert
Alena M. Hutchinson
dblp:237/8750
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › social navigation
crowd navigation |
0.4 | 1 | 2019 | Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019 |
Human-robot interaction › robot navigation
human-aware navigation |
0.4 | 1 | 2019 | Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019 |
Human-robot interaction › robot navigation
social robot navigation |
0.4 | 1 | 2019 | Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment · HRI 2019 |
Usability and user experience research
user experience evaluation |
0.1 | 1 | 2019 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded EnvironmentabstractState-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 |
HRI | 2 |