Demo Data from VLDB 2026 / reviewers in the wild / expert
Mingsheng Zhong
dblp:377/0068
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—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 · 100% | |
| Computer graphics and multimedia
1 paper |
Computational fabrication · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › grasping
adaptive grasping |
0.9 | 1 | 2025 | Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025 |
Robotics › Robot manipulation
grasping |
0.9 | 1 | 2025 | Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025 |
Robotics › Robot manipulation › grasping
soft gripper |
0.9 | 1 | 2025 | Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025 |
Computational fabrication
additive manufacturing |
0.3 | 1 | 2025 | Design of a Novel Pneumatic Soft Gripper for Robust Adaptive Grasping · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
static modeling · 1.7kinematic modeling · 1.7finite element simulation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Design of a Novel Pneumatic Soft Gripper for Robust Adaptive GraspingabstractSoft grippers have shown promising performance in safe and adaptive grasping tasks. However, they often suffer from limitations in grasping force. To address this challenge, this paper presents a novel pneumatic three-finger soft gripper to achieve robust adaptive grasping. The gripper consists of three identical fingers, each containing a pneumatic bending soft actuator and a pneumatic lateral soft actuator. The bending actuator features a tilted pneumatic network structure, which provides superior bending performance compared to traditional vertical pneumatic network structure. The lateral actuator is equipped with three deflection chambers at the finger root to mimic the lateral motions of a human finger. Kinematic and static models are established to predict the bending angle and grasping force of the soft finger under pressurized air. The performance of the proposed soft finger is analyzed through finite element simulations, and the effect of the chamber tilt angle is also examined. The theoretical and simulation results are compared to verify the validity of the analytical models. Finally, the proposed soft gripper is fabricated by 3D printing and molding. Experimental results show that the gripper is capable of grasping various objects of different sizes, shapes, materials, and weights, and can perform dexterous manipulation tasks, such as cap unscrewing. The proposed soft gripper exhibits significant potential for applications in robotic robust grasping tasks. Xiantao Sun, Mingsheng Zhong, Zhouzheng Tang, Weihai Chen |
ICRA | 2 |