Hakjong Noh

Korea Institute of Robot and Convergence

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About

Hakjong Noh is a leading researcher in robot motion planning, with a focus on developing algorithms for systems subject to complex constraints. His most influential contribution is the Tangent Space Rapidly Exploring Random Tree (TS-RRT) algorithm, introduced in his 2011 paper, which has garnered 32 citations. This work addresses a fundamental challenge in robotics: planning motions for robots with holonomic constraints, such as those found in manipulation or locomotion. By constructing random trees directly on constraint manifolds rather than in the full configuration space, Noh’s approach enables efficient navigation of high-dimensional, constrained environments. The TS-RRT algorithm has become a key reference for researchers tackling motion planning in contact-rich tasks, including grasping and assembly. Noh’s work bridges theoretical rigor with practical applicability, offering a scalable solution that reduces computational overhead while maintaining probabilistic completeness. His contributions are particularly valued in the robotics community for advancing the state of the art in constrained motion planning, inspiring further developments in sampling-based algorithms. For students and researchers exploring robot autonomy, Noh’s research provides essential tools for tackling real-world motion planning problems.