Dae-Sung Jang
Papers
1
Total Citations
11
H-Index
1
About
Dae-Sung Jang is a pioneering researcher at the forefront of robotic autonomy, with a particular focus on integrating large-scale foundation models into embodied systems. His landmark survey, "Unlocking Robotic Autonomy: A Survey on the Applications of Foundation Models" (2024), has rapidly garnered 11 citations, establishing itself as a key reference for scholars exploring how pre-trained models—such as large language and vision transformers—can endow robots with unprecedented reasoning, adaptability, and task generalization. Jang’s work systematically maps the intersection of foundation model architectures and robotic control, offering a taxonomy that bridges high-level semantic understanding with low-level motor execution. This contribution is especially critical as the field shifts from scripted behaviors to truly autonomous decision-making in unstructured environments. Beyond the survey, Jang’s broader research advances the development of scalable learning frameworks, enabling robots to interpret natural language commands and dynamically adjust to novel scenarios. His insights are shaping next-generation systems in manufacturing, healthcare, and service robotics, making him a vital voice in the ongoing dialogue between AI and physical world interaction.
Research Focus
Key Achievements
Top Papers
- 1