Papers
7
Total Citations
60
H-Index
4
About
In-Young Ko is a researcher whose work spans adaptive software architectures, intelligent service robotics, and robotic motion planning. Ko's most significant contributions lie in the development of self-managing and self-growing software systems designed to address the inherent dynamism of complex robotic environments — systems that cannot be fully anticipated at design time and must reconfigure themselves in response to changing situational and behavioral demands. Ko's foundational work, including the widely recognized SHAGE framework (2006, 27 citations) and the repository framework for self-growing robot software (2005, 10 citations), established key principles for enabling robots to dynamically adapt their software architectures without human intervention. These contributions were further refined through task-based and resource-aware approaches to generating optimal software configurations for intelligent service robots, ensuring reliable performance under real-world constraints. Later in Ko's career, research expanded into randomized path planning algorithms for complex manipulation tasks — such as robotic suturing, knot tying, and assembly operations — addressing the challenge of object release and regrasping across varied robot configurations. Together, Ko's body of work reflects a sustained commitment to making intelligent robotic systems more autonomous, adaptive, and practically deployable, offering lasting influence across software engineering and robotics research communities.
Research Focus
Key Achievements
Top Papers
- 1SHAGE27 citations · 2006
- 2A repository framework for self-growing robot software10 citations · 2005
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- 5Randomized path planning on foliated configuration spaces4 citations · 2014
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