Demo Data from VLDB 2026 / reviewers in the wild / expert
Steffen Madsen
dblp:260/3565
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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.
| Artificial intelligence
1 paper |
Robot manipulation · 50% Motion planning and robot control · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
assembly |
0.5 | 1 | 2021 | Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual Input · ICRA 2021 |
Robotics › Motion planning and robot control
trajectory planning |
0.5 | 1 | 2021 | Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual Input · ICRA 2021 |
Robotics › Motion planning and robot control
offline programming |
0.1 | 1 | 2021 | Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual Input · ICRA 2021 |
Robotics › Robot manipulation
robot programming |
0.1 | 1 | 2021 | Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual Input · ICRA 2021 |
Methods — techniques the papers use, named apart from their topics
skill-based programming · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Automated Generation of Robot Trajectories for Assembly Processes Requiring Only Sparse Manual InputabstractIn this paper, a new method for offline programming part assembly operations with tight fittings is presented. More specifically, an assembly process trajectory generator with self programming capabilities is developed where the user needs to provide only very sparse and intuitive input. The presented system is added to the existing skill based robot software package VEROSIM. In VEROSIM, the trajectory generator is applied to an industrial test platform for assembling insulin injection devices at the Danish pharmaceutical company Novo Nordisk, where it is shown that the trajectories as expected are executable. Hence, the method is a strong alternative to online approaches such as programming by demonstration. Steffen Madsen, Milad Jami, Henrik Gordon Petersen |
ICRA | 1 |