EDBT 2026 Demo / reviewers in the wild / expert
Ng Cheng Meng
dblp:342/1737
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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 · 87% Segmentation and scene understanding · 13% |
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 › grasping in clutter
bin picking |
0.7 | 1 | 2023 | Two-Stage Grasping: A New Bin Picking Framework for Small Objects · ICRA 2023 |
Robotics › Robot manipulation › grasping
grasp planning |
0.7 | 1 | 2023 | Two-Stage Grasping: A New Bin Picking Framework for Small Objects · ICRA 2023 |
Computer vision › Segmentation and scene understanding
object detection and segmentation |
0.2 | 1 | 2023 | Two-Stage Grasping: A New Bin Picking Framework for Small Objects · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
pushing · 0.7object density estimation · 0.7fine segmentation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Two-Stage Grasping: A New Bin Picking Framework for Small ObjectsabstractThis paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and pushing are performed in the second stage. A small object bin picking system has been realized to exhibit the concept of two-stage grasping. Experiments have shown the effectiveness of the proposed framework. Unlike traditional bin picking methods focusing on vision-based grasping planning using classic frameworks, the challenges of picking cluttered small objects can be solved by the proposed new framework with simple vision detection and planning. Jianshu Zhou, Junda Huang, Yichuan Li 0002, Ng Cheng Meng, Qi Dou 0001, Yun-Hui Liu 0001 |
ICRA | 5 |