Demo Data from SIGIR 2026 / reviewers in the wild / expert
Matti Sirviö
dblp:85/3888
· 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 · 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 · 50% Robot manipulation · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot planning
sensing planning |
0.1 | 1 | 2006 | Planning of Sensory Feedback in Industrial Robot Workcells · ICRA 2006 |
Methods — techniques the papers use, named apart from their topics
a posteriori error covariance matrix · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Planning of Sensory Feedback in Industrial Robot WorkcellsabstractIn this paper, we present a method for planning the sensing features when the geometrical representation of the target object is known. The presented method is a synthesis form and can be used in several measurement applications in robotics. Sensing planning is an important issue when the measurement data is sparse, includes a lot of noise or there are tight time-requirements. The criteria for selecting the measurement locations and orientations is a posteriori error covariance matrix of the parameters to be estimated. The presented approach is verified by simulation tests in the case of work object location. The proposed method has been implemented into a commercial ConiferRob-robot software Mikko Sallinen, Tapio Heikkilä, Matti Sirviö |
ICRA | 3 |