Demo Data from VLDB 2026 / reviewers in the wild / expert
Simon D. Baumgarten
dblp:316/5430
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
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% | |
| Artificial intelligence
1 paper |
Robot manipulation · 50% Robot navigation and mapping · 50% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
service robot |
0.6 | 1 | 2022 | Refilling Water Bottles in Elderly Care Homes With the Help of a Safe Service Robot · HRI 2022 |
Methods — techniques the papers use, named apart from their topics
technical evaluation · 1.1questionnaire · 1.1pose detection · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Refilling Water Bottles in Elderly Care Homes With the Help of a Safe Service RobotabstractThis study presents key technologies of a mobile service robot developed to manipulate objects around people safely. We demonstrate this ability to support staff in elderly care homes in the future. The take-over by a service robot allows the staff to spend less time with routine logistical tasks and therefore better focus on the interaction with residents. In the selected application scenario, the robot helps staff by (1) retrieving empty bottles from the residents' rooms, (2) bringing them to the kitchen, (3) taking the refilled bottles back to a table inside the residents' rooms. This task seems trivial for a person, but the robot needs to orchestrate numerous algorithms and components to work smoothly, such as bottle pose detection, manipulation, and navigation. A technical evaluation indicates a high performance of single components, but due to isolated failures, the overall scenario does not always succeed. Next to the technical aspects, it is fundamental to determine the acceptance of the robot, which was achieved by analyzing questionnaires given to care workers. Finally, this paper presents lessons learned to help other researchers in similar use-cases. Çagatay Odabasi, Florenz Graf, Jochen Lindermayr, Mayank Patel 0001, Simon D. Baumgarten, Birgit Graf |
HRI | 5 |