Demo Data from EDBT 2026 / reviewers in the wild / expert
Oliver Gibbons
dblp:353/5859
· DBLP profile ↗
1ranked-venue papers
1as 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 first-author · 1 since 2021Systems, architecture and hardware · 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 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › assembly
assembly task |
0.7 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Robotics › Robot manipulation › assembly
peg-in-hole insertion |
0.7 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Robotics › Robot manipulation › tactile sensing
tactile feedback control |
0.7 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Robotics › Robot manipulation
tactile sensing |
0.7 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Robotics › Robot manipulation
grasping |
0.2 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Robotics › Robot manipulation
in-hand pose estimation |
0.2 | 1 | 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
force feedback control · 0.7capacitive tactile sensing · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A Tactile Feedback Insertion Strategy for Peg-in-Hole TasksabstractThe Peg-In-Hole (PiH) task performed under un-certain conditions still represents a challenge for autonomous robots. When the peg is not rigidly connected to the robot end-effector, the external forces generated by peg-environment interactions can change the in-hand pose of the peg. This aspect must be taken into account when performing the insertion. This paper deals with this problem and proposes an insertion strategy driven by tactile feedback. In particular, we consider holding the peg using a parallel gripper equipped with tactile sensors, whose measurements are processed to capture in-hand rotations of the peg pose. This information is fed back to the robot controller and used to compensate for changes in the peg orientation and end-point position occurring during the task execution. The approach is validated on a real robot using a two-finger gripper equipped with two capacitive-based tactile sensor arrays hosting 20 tactile elements each. We show that the proposed method achieves an insertion success rate of 38/40 with a 0.1 mm clearance between the peg and hole. Oliver Gibbons, Alessandro Albini, Perla Maiolino |
ICRA | 1 |