Demo Data from SIGIR 2026 / reviewers in the wild / expert
Trinh Van Vinh
dblp:117/2318
· 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
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 |
Robot manipulation · 61% Motion planning and robot control · 39% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
deformable object manipulation |
0.1 | 1 | 2012 | A new strategy for making a knot with a general-purpose arm · ICRA 2012 |
Robotics › Robot manipulation › deformable object manipulation
knot tying |
0.1 | 1 | 2012 | A new strategy for making a knot with a general-purpose arm · ICRA 2012 |
Robotics › Motion planning and robot control › robot learning
manipulation skill learning |
0.1 | 1 | 2012 | A new strategy for making a knot with a general-purpose arm · ICRA 2012 |
Robotics › Motion planning and robot control
trajectory planning |
0.0 | 1 | 2012 | A new strategy for making a knot with a general-purpose arm · ICRA 2012 |
Methods — techniques the papers use, named apart from their topics
skill synthesis · 0.1motion primitive extraction · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | A new strategy for making a knot with a general-purpose armabstractThis paper proposes a new strategy for making a knot with a general-purpose arm. The strategy is divided into four stages: First, a human-performed knotting action is analyzed to acquire some motion primitives. Second, based on these databases, the robot is taught to tie a knot. Third, key points on the stored trajectories are extracted. These key points are considered in a general case, and then used to implement the manipulation skills. Finally, knotting task is generated by synthesis of the manipulation skills. An overhand knot was made by the actual robot for the validity of the proposed method. This paper also describes a system for manipulating deformable objects (e.g. knotting a rope). Trinh Van Vinh, Tetsuo Tomizawa, Shunsuke Kudoh, Takashi Suehiro |
ICRA | 1 |