Demo Data from EDBT 2026 / reviewers in the wild / expert
Martina Pelosi
dblp:353/6087
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
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 · 88% 3D vision · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › deformable object manipulation
cable routing |
0.7 | 1 | 2023 | Tactile based robotic skills for cable routing operations · ICRA 2023 |
Robotics › Robot manipulation › deformable object manipulation
deformable linear object manipulation |
0.7 | 1 | 2023 | Tactile based robotic skills for cable routing operations · ICRA 2023 |
Computer vision › 3D vision
3d shape reconstruction |
0.2 | 1 | 2023 | Tactile based robotic skills for cable routing operations · ICRA 2023 |
Robotics › Robot manipulation
tactile sensing |
0.2 | 1 | 2023 | Tactile based robotic skills for cable routing operations · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
tactile sensing · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Tactile based robotic skills for cable routing operationsabstractThis paper proposes a set of tactile based skills to perform robotic cable routing operations for deformable linear objects (DLOs) characterized by considerable stiffness and constrained at both ends. In particular, tactile data are exploited to reconstruct the shape of the grasped portion of the DLO and to estimate the future local one. This information is exploited to obtain a grasping configuration aligned to the local shape of the DLO, starting from a rough initial grasping pose, and to follow the DLO's contour in the three-dimensional space. Taking into account the distance travelled along the arc length of the DLO, the robot can detect the cable segments that must be firmly grasped and inserted in intermediate clips, continuing then to slide along the contour until the next DLO's portion, that has to be clipped, is reached. The proposed skills are experimentally validated with an industrial robot on different DLOs in several configurations and on a cable routing use case. Andrea Monguzzi, Martina Pelosi, Andrea Maria Zanchettin, Paolo Rocco |
ICRA | 2 |