EDBT 2026 Demo / reviewers in the wild / expert
Theing Mwe Oo
dblp:371/1766
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0004-0515-8728ORCID · reported
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% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
service robot |
0.8 | 1 | 2024 | Iterative Robot Waiter Algorithm Design: Service Expectations and Social Factors · HRI 2024 |
Methods — techniques the papers use, named apart from their topics
within-subjects study · 0.8bodystorming · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Iterative Robot Waiter Algorithm Design: Service Expectations and Social FactorsabstractMobile robots carrying food in restaurants are here. What service behavior norms do people expect them to follow? This paper evaluates robot waiter algorithms and service parameters for a robot serving two participants at a simulated cocktail event, varying body-storming inspired context variables such as: "hunger level" and "relationship to each other," robot delivery algorithms (lead, follow, ambient), and participant pose (standing, seated). In the within-subjects design, pairs of people were given a series of context prompts, and told to participate as felt natural. Output variables included whether they took food and post-trial survey ratings of the robot. The results show a positive correlation between food taking (or feelings of obligation to take food) and human OR robot initiative, relative to a mixed-ambient algorithm with no explicit leader. The robot waiter that initiates is the clearest and most noticeable. There were also some challenges: people in conversation would sometimes forget or delay calls for cupcakes, ambient robot motion was hardest to notice, and bringing food one person ordered to the other was unforgivable. When in doubt, go to the middle. Finally, participants enjoyed the robot spinning, describing it as a dessert tray which attracted their eyes to the robot. Heather Knight, Deanna Flynn, Theing Mwe Oo, Julia Hansen |
HRI | 3 |