Demo Data from VLDB 2026 / reviewers in the wild / expert
Maria B. Ou
dblp:313/7909
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.6 | 1 | 2022 | Shake and Take: Fast Transformation of an Origami Gripper · IEEE Trans. Robotics 2022 |
Robotics › Robot manipulation › robot manipulator
robot arm |
0.2 | 1 | 2022 | Shake and Take: Fast Transformation of an Origami Gripper · IEEE Trans. Robotics 2022 |
Methods — techniques the papers use, named apart from their topics
vertex characterization · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Shake and Take: Fast Transformation of an Origami GripperabstractOrigami structures can transform their form and function by changing the direction of their folds. This reconfiguration can enable multifunctional robots, but doing so requires a fast, robust, and repeatable actuation method. In this article, we present an origami gripper that uses dynamic transformation to change its kinematic behavior in less than a second. We characterize individual vertices to show that the transformation is predictable and repeatable for different designs and orientations. We then apply it to a multivertex template that is capable of a wide range of shapes and motion patterns, indicating that transformation can be generalized to complex and functional machines. To demonstrate this, we built a transforming origami gripper on a robotic arm to pick up multiple objects. Demonstrations show that the gripper can quickly reconfigure between three different grasping modes and has sufficient stiffness to engage with and lift multiple objects with distinct geometries. Chang Liu 0022, Samuel J. Wohlever, Maria B. Ou, Taskin Padir, Samuel M. Felton |
IEEE Trans. Robotics | 3 |