Demo Data from EDBT 2026 / reviewers in the wild / expert
Dan Kruse
dblp:352/0771
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
dual-arm manipulation |
0.9 | 1 | 2025 | In the Wild Ungraspable Object Picking with Bimanual Nonprehensile Manipulation · ICRA 2025 |
Robotics › Robot manipulation
grasping |
0.9 | 1 | 2025 | In the Wild Ungraspable Object Picking with Bimanual Nonprehensile Manipulation · ICRA 2025 |
Robotics › Robot manipulation
nonprehensile manipulation |
0.9 | 1 | 2025 | In the Wild Ungraspable Object Picking with Bimanual Nonprehensile Manipulation · ICRA 2025 |
Robotics › Robot manipulation › grasping › grasp planning
grasp point selection |
0.3 | 1 | 2025 | In the Wild Ungraspable Object Picking with Bimanual Nonprehensile Manipulation · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
visual observation · 0.9friction constraints · 0.9force closure · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | In the Wild Ungraspable Object Picking with Bimanual Nonprehensile ManipulationabstractPicking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with re-trieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67 % success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu. be/gOhOrDuK8jM. Albert Wu, Dan Kruse |
ICRA | 2 |