Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeff Gau
dblp:324/6288
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
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 |
Motion planning and robot control · 67% Legged, aerial and field robots · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › aerial robots
flapping-wing robot |
0.6 | 1 | 2022 | Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022 |
Robotics › Motion planning and robot control › robot control › nonlinear control
limit cycle control |
0.6 | 1 | 2022 | Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022 |
Robotics › Motion planning and robot control
robot control |
0.6 | 1 | 2022 | Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
robophysical model · 0.6feedback control law · 0.6delayed stretch-activation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect MuscleabstractIn most instances, flapping wing robots have emulated the “synchronous” actuation of insects in which the wingbeat timing is generated from a time-dependent, rhythmic signal. The internal dynamics of asynchronous insect flight muscle enable high-frequency, adaptive wingbeats with minimal direct neural control. In this paper, we investigate how the delayed stretch-activation (dSA) response of asynchronous insect flight muscle can be transformed into a feedback control law for flapping wing robots that results in stable limit cycle wingbeats. We first demonstrate - in theory and simulation - the mechanism by which asynchronous wingbeats self-excite. Then, we implement the feedback law on a dynamically-scaled robophysical model as well as on an insect-scale robotic flapping wing. Experiments on large- and small-scale robots demonstrate good agreement with the theory results and highlight how dSA parameters govern wingbeat amplitude and frequency. Lastly, we demonstrate that asynchronous actuation has several advantages over synchronous actuation schemes, including the ability to rapidly adapt or halt wingbeats in response to external loads or collisions through low-level feedback control. Jeff Gau, Simon Sponberg, Nick Gravish |
ICRA | 2 |