EDBT 2026 Demo / reviewers in the wild / expert
Papat Fungtammasan
dblp:23/11183
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Robot manipulation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.1 | 1 | 2012 | Grasp Input Optimization Taking Contact Position and Object Information Uncertainties into Consideration · IEEE Trans. Robotics 2012 |
Robotics › Robot manipulation › grasping
grasp optimization |
0.1 | 1 | 2012 | Grasp Input Optimization Taking Contact Position and Object Information Uncertainties into Consideration · IEEE Trans. Robotics 2012 |
Robotics › Robot manipulation › contact modeling
frictional contact modeling |
0.0 | 1 | 2012 | Grasp Input Optimization Taking Contact Position and Object Information Uncertainties into Consideration · IEEE Trans. Robotics 2012 |
Methods — techniques the papers use, named apart from their topics
linear optimization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Grasp Input Optimization Taking Contact Position and Object Information Uncertainties into ConsiderationabstractThis paper presents a novel approach for grasp optimization considering contact position and object information uncertainties. In practice, it is hard to grasp an object at the designated or planned contact positions, as errors in measurement, estimation, and control usually exist. Therefore, we first formulate the influences of contact uncertainties on joint torques, contact wrenches, and frictional condition. We then include external wrench uncertainties in the required external wrenches set. Based on this formulation, we define the linear grasp optimization problem for two kinds of frictional contact models-frictional point contact and soft finger contact-so that we can successfully grasp an object even if deviations in contact point, object weight, and center of mass occur. The validity of our approach is shown by means of numerical examples and the result of experiments. Papat Fungtammasan, Tetsuyou Watanabe |
IEEE Trans. Robotics | 1 |