Division of Intelligent Robot829
Division of Intelligent Robotics pursues excellence in research through differentiation of technology and securing of leading edge technology. We also play a key role in making regional strategic industries increase their competitiveness in the global market through steady commercialization of our existing technology. Our ultimate goal is to contribute to build a welfare state where humans and robots exist side by side as a flagship research center.
For the branding of DGIST’s robot, we conduct R&D focusing on the fields that aligns with DGIST's core research fields and the promotion plan of regional strategic industries. In other words, develop intelligent components, modules, and service technology that can be common differentiated solutions by selecting the fields of smart health, active rehabilitation, and smart factory as priority areas for both key R&D and technology commercialization.
Our key role is to resolve national and global issues through convergence research and differentiated application research of core robot technology and intelligent sensor technology.
DGIST Division of Intelligent Robot Homepage
For the branding of DGIST’s robot, we conduct R&D focusing on the fields that aligns with DGIST's core research fields and the promotion plan of regional strategic industries. In other words, develop intelligent components, modules, and service technology that can be common differentiated solutions by selecting the fields of smart health, active rehabilitation, and smart factory as priority areas for both key R&D and technology commercialization.
Our key role is to resolve national and global issues through convergence research and differentiated application research of core robot technology and intelligent sensor technology.
DGIST Division of Intelligent Robot Homepage
Recent Submissions
- Logical Anomaly Detection with Text-based Logic via Component-Aware Contrastive Language-Image Training
- A Novel Multi-parametric H∞ Filter Design Method for Imperfectly Reconstructed Lateral Vehicle Dynamics
- MOSInversion: Knowledge distillation-based incremental learning in organ segmentation using DeepInversion
- FFT-기반 Partition Selection을 활용한 SP-MUSIC DOA 추정 성능 개선
- Noise-Resilient Masked Face Detection Using Quantized DnCNN and YOLO
