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Ainoor Teimoorzadeh

dblp:326/1367 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › nonprehensile manipulation
dynamic object transport
0.912025
Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025
Robotics › Motion planning and robot control
motion planning
0.912025
Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025
Robotics › Robot manipulation
nonprehensile manipulation
0.912025
Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach · ICRA 2025
Robotics › Motion planning and robot control
trajectory optimization
0.912025
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
YearPublicationVenuePosition
2025 Robust Nonprehensile Dynamic Object Transportation: A Closed-Loop Sensitivity Approach
abstract
In 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
ICRA1