Jaehun Kim

Pohang University of Science and Technology, Korea Institute of Science and Technology, Delft University of Technology

Papers

Total Citations

H-Index

About

Jaehun Kim is a researcher at the intersection of robotics, artificial intelligence, and intelligent systems, with key contributions in tactile sensing, autonomous inspection, and creative AI. His most cited work introduces a spiking neural network with unsupervised learning for object shape recognition using tactile sensor arrays—a foundational step toward enabling robotic hands to process tactile information for dexterous manipulation. This paper has garnered 11 citations, reflecting its growing influence in the field of robotic perception. Earlier, Kim addressed critical infrastructure safety through a vision-based automatic inspection system for power transmission lines, achieving 10 citations by proposing a real-time alternative to dangerous manual inspections. More recently, he has ventured into the creative domain with a generative autoregressive network that synthesizes 3D dance moves from music, integrating music feature encoding, pose generation, and genre classification. This work, with 5 citations, showcases his versatility in applying machine learning to human-robot interaction and entertainment. Kim’s research spans from fundamental tactile processing to applied autonomous systems and generative AI, demonstrating a broad impact on both industrial automation and creative technologies.