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About

Yongsik Jin is a leading researcher in developmental robotics, autonomous systems, and human-robot interaction, with a focus on perception-action learning and depth estimation. His most influential work, "Enhancing Binocular Depth Estimation Based on Proactive Perception and Action Cyclic Learning for an Autonomous Developmental Robot" (21 citations), introduces a novel cyclic learning framework that mimics human perception-action loops to improve depth accuracy in humanoid robots. Jin has made significant contributions to monocular depth estimation from fisheye cameras using knowledge distillation (9 citations), advancing collision avoidance in autonomous driving and robotics. His research on uncertainty-aware knowledge distillation for collision identification in collaborative robots (9 citations) has enhanced safety in industrial human-robot interaction. Additionally, Jin has pioneered sampled-data state estimation for LSTM neural networks (18 citations), bridging continuous-time dynamics with irregularly sampled outputs. His work on uncertainty-aware depth networks for visual-inertial odometry (6 citations) further demonstrates his impact on mobile robot localization. With over 65 total citations, Jin's interdisciplinary approach integrates deep learning, control theory, and developmental psychology, making him a notable figure in autonomous robotics and safe human-robot collaboration.