Jaein Lim
Papers
3
Total Citations
15
H-Index
2
About
Jaein Lim is a robotics researcher whose work lies at the intersection of motion planning, perception, and autonomous decision-making. His primary research areas include incremental search algorithms, replanning under dynamic environments, and information-theoretic environment modeling. Lim’s most notable contribution is the development of "Class-Ordered LPA*," an incremental-search algorithm for weighted colored graphs that enables robots to efficiently reuse prior search results when environmental conditions change. This work, with 11 citations, addresses a fundamental challenge in robotics: maintaining responsive and robust autonomy in complex, dynamic settings. Building on this foundation, Lim introduced "Lifelong-GLS" (L-GLS) and its bounded suboptimal variant, which combine the strengths of incremental and lazy search algorithms to achieve fast replanning with guaranteed performance bounds. In the perception domain, Lim has advanced autonomous systems by developing an information-theoretic approach to compress semantic octree models from raw point-cloud data, enabling integrated perception and planning. His work bridges the gap between efficient computation and rich environmental understanding, making him a rising contributor to the field of autonomous robotics.
Research Focus
Key Achievements
Top Papers
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