Intelligence and Control for Robots Laboratory
Il-Hong Suh Emeritus Professor
ihsuh@hanyang.ac.kr
Subject
Random Variable, Control Engineering
Computational Intelligence Theory, Probabilistic Intelligence, Special Topics on Intelligent Systems
Education
Ph.D., KAIST, Korea
Career
2016.1 ~ Present Fellow, IEEE
2015.3 ~ Present President, Brain Engineering Society of Korea
2013.10 ~ Present Professor, Dept. of Electronic Engineering, Hanyang University
2013.1 ~ Present Senior Member, NAEK
2010.6 ~ Present Editor-in-Chief, International Journal of Intelligent Service Robotics (Springer)
2010.6 ~ 2014.6 Associate Editor, IEEE Transactions on Robotics
2009.5 ~ Present Leader, National Robotics-Specialized Education Consortium (RoSEC)
2008 President, Korea Robotics Society
2000.3 ~ 2010.9 Professor, Dept. of Computer Science and Engineering, Hanyang University
1985.3 ~ 2000.2 Professor, Dept. of Electronic Engineering, Hanyang University (Erica)
2015.3 ~ Present President, Brain Engineering Society of Korea
2013.10 ~ Present Professor, Dept. of Electronic Engineering, Hanyang University
2013.1 ~ Present Senior Member, NAEK
2010.6 ~ Present Editor-in-Chief, International Journal of Intelligent Service Robotics (Springer)
2010.6 ~ 2014.6 Associate Editor, IEEE Transactions on Robotics
2009.5 ~ Present Leader, National Robotics-Specialized Education Consortium (RoSEC)
2008 President, Korea Robotics Society
2000.3 ~ 2010.9 Professor, Dept. of Computer Science and Engineering, Hanyang University
1985.3 ~ 2000.2 Professor, Dept. of Electronic Engineering, Hanyang University (Erica)
Intelligence and Control for Robots
Laboratory
1. Planning- - Reactive Planning: Planning by situated selections of embodied
- behaviors
- - Proactive Planning: Planning of required actions by predicting
- coming situations
- - Improvisational Planning: Planning of alternatives in
- unexpected situations using local information
- 2. Manipulation
- - Skill learning by imitation: Learning complex and precise
- human-level manipulation skills
- - Autonomous primitive skill learning: Autonomous learning of
- basis skills in given tasks
- - Grammaticalization: Techniques allowing reuse of the learned
- basis skills in various ways, as words in languages
- 3. Navigation
- - Line-based indoor SLAM: SLAM using straight lines in
- manmade environments
- - Navigation imitating humans: Localization, Path integration,
- Reorientation by scenes
- - Semantic SLAM: Mapping by integrating topology and
- semantics
- 4. Recognition
- - Hierarchical, Interactive Recognition and Segmentation
- framework
- - Neo-cortex theory based primitive feature extraction:
- Oriented Edge-Selective Band-Pass Filtering
- - Neo-cortex theory based object segmentation
- Research
- Cognitive robotics and control
