Demo Data from EDBT 2026 / reviewers in the wild / expert
Arshat Urazbayev
dblp:246/7938
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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% | |
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › tactile sensing › contact sensing
contact localization |
0.4 | 1 | 2019 | Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019 |
Robotics › Robot manipulation
tactile sensing |
0.4 | 1 | 2019 | Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019 |
Haptics and multimodal interaction › tactile sensing
artificial skin |
0.1 | 1 | 2019 | Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
hierarchical classification · 0.8classical machine learning · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot SkinabstractThe sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, incorporating plastic optical fibers (POF), transparent silicone rubber and an off-the-shelf color camera. Processing electronics are placed away from the sensing surface to make the sensor robust to harsh environments. Contact localization is possible thanks to the lower number of light sources compared to the number of camera POFs. Classical machine learning techniques and a hierarchical classification scheme were used for contact localization. Specifically, we generated the mapping from stimulation to sensation of a robotic perception system using our sensor. We achieved a force sensing range up to 18 N with the force resolution of around 3.6 N and the spatial resolution of 8 mm. The color-coded tactile sensor is suitable for tactile exploration and might enable further innovations in robust tactile sensing. Zhanat Kappassov, Daulet Baimukashev, Zharaskhan Kuanyshuly, Yerzhan Massalin, Arshat Urazbayev, Huseyin Atakan Varol |
ICRA | 5 |