Demo Data from VLDB 2026 / reviewers in the wild / expert
Congjian Li
dblp:211/5734
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
motion control |
0.5 | 1 | 2021 | Robot Motion Control with Compressive Feedback · ICRA 2021 |
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing |
0.5 | 1 | 2021 | Robot Motion Control with Compressive Feedback · ICRA 2021 |
Methods — techniques the papers use, named apart from their topics
set-based feedback · 0.5compressive sensing · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Robot Motion Control with Compressive FeedbackabstractRobot motion control aims to generate control inputs for a robotic system to track a planned trajectory. Feedback provided by sensors plays an essential role in motion control by improving system performance when external disturbances and/or initial errors exist. However, feedback signals, such as images are often of a large size, which imposes a heavy computational burden on the system. In this paper, a new robot motion control scheme is proposed based on compressive feedback to improve feedback rate. The controller is designed in non-vector space using compressive feedback. As an application, visual servoing is formulated under the proposed framework by considering a feedback image as a set, instead of a traditional feature vector. Experiments are conducted to validate the proposed scheme. Congjian Li, Yisheng Guan, Ning Xi 0001 |
ICRA | 1 |