EDBT 2026 Demo / reviewers in the wild / expert
Ainoor Teimoorzadeh
dblp:326/1367
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
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 · 50% Motion planning and robot control · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › nonprehensile manipulation
dynamic object transport |
0.9 | 1 | 2025 | Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025 |
Robotics › Motion planning and robot control
motion planning |
0.9 | 1 | 2025 | Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025 |
Robotics › Robot manipulation
nonprehensile manipulation |
0.9 | 1 | 2025 | Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025 |
Robotics › Motion planning and robot control
trajectory optimization |
0.9 | 1 | 2025 | Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
stochastic optimization · 0.9closed-loop sensitivity · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity ApproachabstractIn this paper, we propose a closed-loop sensitivity-based approach to enhance the robustness of robotic non-prehensile dynamic manipulation tasks. The proposed method aims at fulfilling the transportation of an object, that is free to move on a tray-shaped robot end-effector, in face of not perfectly known nominal dynamic parameters. The approach is built up on taking the parameterized reference trajectory to be tracked as the optimization variable minimizing a norm of the task closed-loop sensitivity. The resulting optimal reference trajectory is inherently more robust to the parametric variations of object dynamic properties compared to a baseline trajectory execution. The tracking performance is assessed and validated along hardware experiments and an extensive simulation campaign assessing the superior robustness of our approach. Ainoor Teimoorzadeh, Andrea Pupa, Mario Selvaggio, Sami Haddadin |
ICRA | 1 |