Demo Data from EDBT 2026 / reviewers in the wild / expert
Lars Overgaard
dblp:63/2027
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1996
—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 · 61% Multi-agent systems · 30% Motion planning and robot control · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.0 | 1 | 1996 | A multi-agent framework for grasping using visual servoing and collision avoidance · ICRA 1996 |
Knowledge, reasoning and agents › Multi-agent systems › multi-agent control
multi-agent robot control |
0.0 | 1 | 1996 | A multi-agent framework for grasping using visual servoing and collision avoidance · ICRA 1996 |
Robotics › Robot manipulation › grasping › vision-based grasping
visual servoing for grasping |
0.0 | 1 | 1996 | A multi-agent framework for grasping using visual servoing and collision avoidance · ICRA 1996 |
Robotics › Motion planning and robot control
collision avoidance |
0.0 | 1 | 1996 | A multi-agent framework for grasping using visual servoing and collision avoidance · ICRA 1996 |
Methods — techniques the papers use, named apart from their topics
visual servoing · 0.0distributed agent control · 0.0collision avoidance · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | A multi-agent framework for grasping using visual servoing and collision avoidanceabstractThis paper describes a multi-agent approach to grasping that combines collision avoidance and visual servoing strategies. Control of the system is distributed among five different types of agents: link agents, joint agents, end-effector agents, task agents, and object agents. As each agent attempts to achieve a desired individual behavior, the manipulator itself exhibits an emergent behavior that avoids obstacles while approaching an object to be grasped. As the object is approached, object agents that represent the object to be grasped and the gripper performing the grasp are instantiated. The instantiation of each of these two object agents, which are guided by visual feedback, automatically changes the manipulator control structure. Once both object agents become active, closed-loop visual servoing of the end-effector during the final stage of grasping is realized. Experimental results are presented that illustrate the three types of manipulator control realized during a typical grasping task. Lars Overgaard, Bradley J. Nelson, Pradeep K. Khosla |
ICRA | 1 |