Demo Data from EDBT 2026 / reviewers in the wild / expert
Florian Vaussard
dblp:95/10005
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Systems, architecture and hardware · 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 · 83% Usability and user experience research · 8% Design research and methods · 8% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
child-robot interaction |
0.2 | 1 | 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger" · HRI 2014 |
Human-robot interaction › robot design
robot behavior design |
0.2 | 1 | 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger" · HRI 2014 |
Human-robot interaction
social robot |
0.2 | 1 | 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger" · HRI 2014 |
Usability and user experience research
user study |
0.1 | 1 | 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger" · HRI 2014 |
Design research and methods › research methodology
wizard of oz prototyping |
0.1 | 1 | 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger" · HRI 2014 |
Methods — techniques the papers use, named apart from their topics
wizard-of-oz · 0.2video analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger"abstractWe present the design approach and evaluation of our prototype called "Ranger". Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evaluated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children's interaction with the robot and the tidying behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our findings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting. Julia Fink, Séverin Lemaignan, Pierre Dillenbourg, Philippe Rétornaz, Florian Vaussard, Alain Berthoud, Francesco Mondada, Florian Wille, Karmen Franinovic |
HRI | 5 |
| 2010 | The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic researchabstractCollective and swarm robotics explores scenarios involving many robots running at the same time. A good platform for collective-robotic experiments should provide certain features among others: it should have a large battery life, it should be able to perceive its peers, and it should be capable of interacting with them. This paper presents the marXbot, a miniature mobile robot that addresses these needs. The marXbot uses differential-drive treels to provide rough-terrain mobility. The marXbot allows continuous experiments thanks to a sophisticated energy management and a hotswap battery exchange mechanism. The marXbot can self-assemble with peers using a compliant attachment mechanism. The marXbot provides high-quality vision, using two cameras directly interfaced with an ARM processor. Compared to the related work, the marXbot has better energy management, vision, and interaction capabilities. By allowing complex tasks in large environments for long durations, the marXbot opens new perspectives for the collective-robotic research. Michael Bonani, Valentin Longchamp, Stéphane Magnenat, Philippe Rétornaz, Daniel Burnier, Gilles Roulet, Florian Vaussard, Hannes Bleuler, Francesco Mondada |
IROS | 7 |