Demo Data from EDBT 2026 / reviewers in the wild / expert
R. Lewczyk
dblp:147/3059
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1984
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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.
| Artificial intelligence
1 paper |
Motion planning and robot control · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
adaptive control |
0.0 | 1 | 1984 | Adaptive path control of a manipulator with visual information · ICRA 1984 |
Robotics › Motion planning and robot control
robot control |
0.0 | 1 | 1984 | Adaptive path control of a manipulator with visual information · ICRA 1984 |
Robotics › Motion planning and robot control › robot control › adaptive control
self-tuning control |
0.0 | 1 | 1984 | Adaptive path control of a manipulator with visual information · ICRA 1984 |
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing |
0.0 | 1 | 1984 | Adaptive path control of a manipulator with visual information · ICRA 1984 |
Methods — techniques the papers use, named apart from their topics
time-series models · 0.0online parameter estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1984 | Adaptive path control of a manipulator with visual informationabstractA picture of a scene is used to extract information for an adaptive control algorithm. The object of interest is first located by means of a classifier. The position and orientation of the object are determined from a binary picture. The desired path which the gripper of the manipulator is to follow is specified by discrete points, first is the Cartesian and then in the joint coordinate system. The adaptive self-tuning controller is outlined. The controller design is based on time series difference equation model, in which the parameters are estimated on-line. The gains of the controller are tuned so that a quadratic performance criteria is minimized. The performance of the system designed is then tested experimentally. Results of these experiments are presented. Antti J. Koivo, R. Lewczyk, T. H. Chiu |
ICRA | 2 |