Demo Data from EDBT 2026 / reviewers in the wild / expert
Jennifer Shum
dblp:196/8400
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved
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.
| Artificial intelligence
1 paper |
Legged, aerial and field robots · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › legged robots
legged robot locomotion |
0.4 | 1 | 2020 | Scaling down an insect-size microrobot, HAMR-VI into HAMR-Jr · ICRA 2020 |
Robotics › Legged, aerial and field robots › mobile robot locomotion
microrobot locomotion |
0.4 | 1 | 2020 | Scaling down an insect-size microrobot, HAMR-VI into HAMR-Jr · ICRA 2020 |
Methods — techniques the papers use, named apart from their topics
open-loop locomotion characterization · 0.4design and fabrication · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Scaling down an insect-size microrobot, HAMR-VI into HAMR-JrabstractHere we present HAMR-Jr, a 22.5mm, 320mg quadrupedal microrobot. With eight independently actuated degrees of freedom, HAMR-Jr is, to our knowledge, the most mechanically dexterous legged robot at its scale and is capable of high-speed locomotion (13.91bodylengthss-1) at a variety of stride frequencies (1-200Hz) using multiple gaits. We achieved this using a design and fabrication process that is flexible, allowing scaling with minimum changes to our workflow. We further characterized HAMR-Jr's open-loop locomotion and compared it with the larger scale HAMR-VI microrobot to demonstrate the effectiveness of scaling laws in predicting running performance. Kaushik Jayaram, Jennifer Shum, Sam Castellanos, E. Farrell Helbling, Robert J. Wood |
ICRA | 2 |