Jinkyu Kim
Papers
6
Total Citations
45
H-Index
3
About
Jinkyu Kim is a roboticist whose research advances motion planning and control for complex robotic systems, from open-chain manipulators to assistive exoskeletons. His most cited work, "An Adaptive Stepsize RRT Planning Algorithm for Open-Chain Robots" (33 citations), tackles a fundamental challenge in sampling-based planning: eliminating the tedious, problem-dependent tuning of step sizes. By introducing an adaptive mechanism, Kim’s algorithm dramatically improves efficiency and usability, making RRT-based planning more practical for real-world applications. He has also pioneered randomized planning for tasks requiring object release and regrasp—critical for robotic suturing, knot tying, and assembly in cluttered environments—and conducted a comparative analysis of energy-based criteria for dynamics-based motion optimization, providing a principled framework for minimizing torque, power loss, and friction in trajectory design. More recently, Kim has expanded into dense visual SLAM (LRSLAM, 2024) and multi-modal locomotion mode recognition for robotic hip exoskeletons (2025), demonstrating a commitment to translating planning theory into real-world assistive technology. His work bridges algorithmic innovation and practical deployment, with growing impact across motion planning, manipulation, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1An Adaptive Stepsize RRT Planning Algorithm for Open-Chain Robots33 citations · 2017
- 2Randomized path planning on foliated configuration spaces4 citations · 2014
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