Ng Cheng Meng

dblp:342/1737 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › grasping › grasping in clutter
bin picking
0.712023
Two-Stage Grasping: A New Bin Picking Framework for Small Objects · ICRA 2023
Robotics › Robot manipulation › grasping
grasp planning
0.712023
Two-Stage Grasping: A New Bin Picking Framework for Small Objects · ICRA 2023
Computer vision › Segmentation and scene understanding
object detection and segmentation
0.212023
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
YearPublicationVenuePosition
2023 Two-Stage Grasping: A New Bin Picking Framework for Small Objects
abstract
This 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
ICRA5