EDBT 2026 Demo / reviewers in the wild / expert
Deborah C. Russell
dblp:33/5628
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—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 · 70% Human-AI interaction · 30% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › automation
automation levels |
0.1 | 1 | 2006 | An investigation of real world control of robotic assets under communication latency · HRI 2006 |
Human-robot interaction › teleoperation
supervisory control |
0.1 | 1 | 2006 | An investigation of real world control of robotic assets under communication latency · HRI 2006 |
Human-robot interaction
teleoperation |
0.1 | 1 | 2006 | An investigation of real world control of robotic assets under communication latency · HRI 2006 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | An investigation of real world control of robotic assets under communication latencyabstractRobots are already being used in a variety of applications, including the military battlefield. As robotic technology continues to advance, those applications will increase, as will the demands on the associated network communication links. Two experiments investigated the effects of communication latency on the control of a robot across four Levels Of Automation (LOAs), (1) full teleoperation, (2) guarded teleoperation, (3) autonomous obstacle avoidance, and (4) full autonomy. Latency parameters studied included latency duration, latency variability, and the "direction" in which the latency occurs, that is from user-to-robot or from robot-to-user. The results indicate that the higher the LOA, the better the performance in terms of both time and number of errors made, and also the more resistant to the degrading effects of latency. Subjective reports confirmed these findings. Implications of constant vs. variable-latency, user-to-robot vs. robot-to-user latency, and latency duration are also discussed. Jason P. Luck, Patricia L. McDermott, Laurel Allender, Deborah C. Russell |
HRI | 4 |