EDBT 2026 Demo / reviewers in the wild / expert
Sujeong Yoo
dblp:289/3087
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Robot manipulation · 33% Reinforcement learning · 33% Motion planning and robot control · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
assembly |
0.5 | 1 | 2021 | Learning-Based Automation of Robotic Assembly for Smart Manufacturing · Proc. IEEE 2021 |
Machine learning › Reinforcement learning › imitation learning
learning from observation |
0.5 | 1 | 2021 | Learning-Based Automation of Robotic Assembly for Smart Manufacturing · Proc. IEEE 2021 |
Robotics › Motion planning and robot control › robot learning
robot skill learning |
0.5 | 1 | 2021 | Learning-Based Automation of Robotic Assembly for Smart Manufacturing · Proc. IEEE 2021 |
Computational science and engineering › manufacturing automation › manufacturing
smart manufacturing |
0.1 | 1 | 2021 | Learning-Based Automation of Robotic Assembly for Smart Manufacturing · Proc. IEEE 2021 |
Methods — techniques the papers use, named apart from their topics
simulated retargeting · 1.0imitation learning · 1.0action planning · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Learning-Based Automation of Robotic Assembly for Smart ManufacturingabstractFor smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and “robotic embodiment of learned assembly tasks in the form of skills.” In the former, robots observe human assembly operations to learn a sequence of assembly tasks, which is formalized into a human assembly script. The latter transforms the human assembly script into a robot assembly script in which a sequence of robot-executable assembly tasks are defined based on action planning supported by workspace modeling and simulated retargeting. The assembly tasks, in the form of the robot assembly script, are then implemented via pretrained robot skills. These skills aim to enable robots to execute difficult tasks that involve inherent uncertainties and variations. We validate the proposed approach by building a prototype of the automated robotic assembly system for a power breaker and an electronic set-top box. The results verify that the proposed automated robotic assembly system is not only feasible but also viable, as it is associated with a dramatic reduction in the human effort required for automating robotic assembly. Sang-Hoon Ji, Sukhan Lee 0001, Sujeong Yoo, Il Hong Suh, In-So Kweon, Frank C. Park 0001, Sang Hyoung Lee, Hongseok Kim |
Proc. IEEE | 3 |