Hyoungkyun Kim

Pohang University of Science and Technology, Samsung (South Korea)

Papers

Total Citations

205

H-Index

About

Hyoungkyun Kim is a leading researcher in robotics, with a primary focus on enhancing safety and interaction capabilities in collaborative and contact-rich robotic systems. His most impactful work, "Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach" (2019, 180 citations), revolutionized human-robot collaboration by introducing a deep learning framework for reliable collision detection, addressing a critical safety bottleneck in industrial settings. Kim’s contributions extend to foundational robotics problems, including simultaneous localization and mapping (SLAM), where his work on "Exactly Rao-Blackwellized unscented particle filters for SLAM" (2011) improved state estimation accuracy by overcoming the overconfidence issue in conventional filters. He has also advanced tactile sensing and force interaction, notably through methods for contact force decomposition using pressure distribution and estimating deformed surface displacement from tactile data—enabling robots to safely handle and manipulate objects with precision. Kim’s research bridges deep learning, estimation theory, and physical interaction, yielding practical solutions for safer, more intelligent robots. His work is widely cited and continues to influence the design of collaborative and contact-aware robotic systems.