Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jeremy Stolarz
dblp:77/3403
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 · 77% Human-AI interaction · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
learning from demonstration |
0.1 | 1 | 2007 | Interactive robot task training through dialog and demonstration · HRI 2007 |
Human-AI interaction › conversational interaction › natural language dialogue
spoken dialogue interaction |
0.0 | 1 | 2007 | Interactive robot task training through dialog and demonstration · HRI 2007 |
Methods — techniques the papers use, named apart from their topics
speech recognition · 0.1dialog interpretation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Interactive robot task training through dialog and demonstrationabstractEffective human/robot interfaces which mimic how humans interact with one another could ultimately lead to robots being accepted in a wider domain of applications. We present a framework for interactive task training of a mobile robot where the robot learns how to do various tasks while observing a human. In addition to observation, the robot listens to the human's speech and interprets the speech as behaviors that are required to be executed. This is especially important where individual steps of a given task may have contingencies that have to be dealt with depending on the situation. Finally, the context of the location where the task takes place and the people present factor heavily into the robot's interpretation of how to execute the task. In this paper, we describe the task training framework, describe how environmental context and communicative dialog with the human help the robot learn the task, and illustrate the utility of this approach with several experimental case studies. Paul E. Rybski, Kevin Yoon, Jeremy Stolarz, Manuela M. Veloso |
HRI | 3 |