EDBT 2026 Demo / reviewers in the wild / expert
Sha Ye
dblp:135/8722
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · unresolved
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 · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.2 | 1 | 2013 | Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude · ICRA 2013 |
Robotics › Robot manipulation › grasping › grasp stability
grasp robustness |
0.2 | 1 | 2013 | Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude · ICRA 2013 |
Methods — techniques the papers use, named apart from their topics
proximity sensing · 0.2pre-shaping · 0.2object positioning · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitudeabstractIn this paper, we focus on unknown parameters such as the position and attitude of the object, and describe a short-range, high-speed and noncontact sensing method for obtaining the position and attitude of the object using IR Net-Structure Proximity Sensor (“IR-NSPS”) which complements the dead region of sensory information between visual and tactile sensing. To be more precise, we propose two effective control methods which are pre-shaping and object positioning using IR-NSPS for robust grasping by adjusting the gripper configuration in response to attitude error of up to ±45 deg and the position error of up to ±80 mm of the unknown object. The methods therefore can significantly increase the speed and effectiveness of grasping objects without requiring a specific approach that depends on a vision sensor. Furthermore, to demonstrate the advantages of pre-shaping and object positioning, object grasping experiments were performed using these two operations to grasp objects placed randomly on a tabletop. Sha Ye, Yosuke Suzuki, Masatoshi Ishikawa, Makoto Shimojo |
ICRA | 1 |