Demo Data from EDBT 2026 / reviewers in the wild / expert
Kadon Engle
dblp:226/6444
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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.
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 77% Human-robot interaction · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › object manipulation
grasping |
0.3 | 1 | 2018 | Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object Size · ICRA 2018 |
Methods — techniques the papers use, named apart from their topics
survey data collection · 0.3confidence-interval polytope · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object SizeabstractThis paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size. Ammar Kothari, John Morrow, Victoria Thrasher, Kadon Engle, Ravi Balasubramanian, Cindy Grimm |
ICRA | 4 |