Demo Data from SIGIR 2026 / reviewers in the wild / expert
Michael L. Chen
dblp:47/3216
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 · 62% Motion planning and robot control · 19% Reinforcement learning · 19% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › assembly
multi-robot assembly |
0.0 | 1 | 2000 | Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000 |
Machine learning › Reinforcement learning › multi-agent reinforcement learning
multi-agent communication |
0.0 | 1 | 2000 | Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000 |
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing |
0.0 | 1 | 2000 | Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000 |
Methods — techniques the papers use, named apart from their topics
visual servoing · 0.0low-latency protocols · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Visually Guided Coordination for Distributed Precision AssemblyabstractWe document our initial efforts to instantiate visually-guided cooperative behaviors between robotic agents in the minifactory environment. The minifactory incorporates high-precision, 2-DOF robotic agents to perform micron-level precision 4-DOF assembly tasks. Here we utilize two minifactory agents to perform visual servoing. We present a detailed description of the control and communication systems used to coordinate the agents. To provide a suitable communications infrastructure, we describe the development of a new interagent communication system which uses low-latency protocols carried by a commercial 100 Mb Ethernet network. Finally, we conclude by presenting experimental results front our first coordinated multi-agent task, the visually guided positioning of a small medical device. Michael L. Chen, Shinji Kume, Alfred A. Rizzi, Ralph L. Hollis |
ICRA | 1 |