Demo Data from SIGIR 2026 / reviewers in the wild / expert
Lac Van Duong
dblp:242/0700
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0001-8586-7057ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › grasping › grasp analysis
force distribution |
0.5 | 1 | 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation · IEEE Trans. Robotics 2021 |
Robotics › Robot manipulation
tactile sensing |
0.5 | 1 | 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation · IEEE Trans. Robotics 2021 |
Robotics › Robot manipulation › tactile sensing
vision-based tactile sensing |
0.5 | 1 | 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation · IEEE Trans. Robotics 2021 |
Robotics › Robot manipulation › tactile sensing › tactile skin
whole-body tactile sensing |
0.5 | 1 | 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation · IEEE Trans. Robotics 2021 |
Robotics › Robot manipulation
soft robotics |
0.1 | 1 | 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation · IEEE Trans. Robotics 2021 |
Methods — techniques the papers use, named apart from their topics
stereo camera marker tracking · 0.5finite element model · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and EvaluationabstractThe sense of touch allows individuals to physically interact with and better perceive their environment. Touch is even more crucial for robots, as robots equipped with thorough tactile sensation can more safely interact with their surroundings, including humans. This article describes a recently developed large-scale tactile sensing system for a robotic link, called TacLINK, which can be assembled to form a whole-body tactile sensing robot arm. The proposed system is an elongated structure comprising a rigid transparent bone covered by continuous artificial soft skin. The soft skin of TacLINK not only provides tactile force feedback but can change its form and stiffness by inflation at low pressure. Upon contact with the surrounding environment, TacLINK perceives tactile information through the three-dimensional (3-D) deformation of its skin, resulting from the tracking of an array of markers on its inner wall by a stereo camera located at both ends of the transparent bone. A finite element model (FEM) was formulated to describe the relationship between applied forces and the displacements of markers, allowing detailed tactile information, including contact geometry and distribution of applied forces, to be derived simultaneously, regardless of the number of contacts. TacLINK is scalable in size, durable in operation, and low in cost, as well as being a high-performance system, that can be widely exploited in the design of robotic arms, prosthetic arms, and humanoid robots, etc. This article presents the design, modeling, calibration, implementation, and evaluation of the system. Lac Van Duong, Van Anh Ho |
IEEE Trans. Robotics | 1 |