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About

Jinwook Huh is a robotics researcher whose work spans motion planning, mobile robot navigation, and robotic manipulation — fields where his contributions have meaningfully advanced the state of the art. He is perhaps best known for his pioneering work on learning-guided sampling strategies for motion planning, most notably his 2018 paper "Learning Implicit Sampling Distributions for Motion Planning," which has garnered 67 citations and demonstrated that incorporating prior knowledge into sampling-based planners can dramatically improve efficiency over traditional uniform exploration. This line of research extended into Q-learning-guided Rapidly-exploring Random Trees and adaptive Gaussian Mixture Models for collision-aware planning, collectively reflecting a sustained effort to make motion planners smarter and faster in high-dimensional spaces. Beyond planning algorithms, Huh has contributed to real-world robotic systems, including Team THOR's entry in the prestigious 2015 DARPA Robotics Challenge Finals, showcasing his experience with full-stack disaster response robotics. His more recent work — including RAMP, Pick2Place, and HIO-SDF — demonstrates a forward-looking focus on reactive manipulation, task-aware grasping, and scalable environmental representations. Earlier work on invisible barcode-based indoor localization highlights his longstanding interest in practical navigation solutions. Across more than a decade of research, Huh has established himself as a versatile contributor to intelligent, adaptive robotics.