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Jeremy Stolarz

dblp:77/3403 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Human-robot interaction
learning from demonstration
0.112007
Interactive robot task training through dialog and demonstration · HRI 2007
Human-AI interaction › conversational interaction › natural language dialogue
spoken dialogue interaction
0.012007
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
YearPublicationVenuePosition
2007 Interactive robot task training through dialog and demonstration
abstract
Effective 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
HRI3