Demo Data from EDBT 2026 / reviewers in the wild / expert
Martin Prüfer
dblp:03/5195
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Motion planning and robot control · 62% Probabilistic and Bayesian machine learning · 38% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › system identification
robot dynamics identification |
0.0 | 1 | 1994 | Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression
least squares regression |
0.0 | 1 | 1994 | Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
parameter estimation |
0.0 | 1 | 1994 | Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994 |
Methods — techniques the papers use, named apart from their topics
signal differentiation · 0.0least-squares estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | Identification of Robot Dynamics with Differential and Integral Models: A ComparisonabstractCompares the integral model suggested by Gautier and Khalil (1988, 1989) for identification of robot dynamics with the differential equations of the robot. The advantage of identification by the integral model is, that no measurement of acceleration is required. Although appealing at a first glance, this approach yields problems in conjunction with the necessary least squares algorithm for parameter estimation. The authors even succeed in getting better results by employing the differential equations, without measurement of acceleration. It is shown, how to achieve reasonable good results with low noise by just derivating the position signals. Finally, the authors' results are illustrated by experiments with a direct driven two link robot.> Martin Prüfer, Friedrich M. Wahl |
ICRA | 1 |