Papers
40
Total Citations
354
H-Index
11
About
Youngshik Kim is a robotics engineer and researcher whose work spans human motion analysis, smart material actuators, and mobile robot control. His research has made significant contributions across three interconnected domains: biomechanically inspired robotics, shape memory alloy (SMA) actuation systems, and kinematic control of mobile and multi-robot systems. Kim's most-cited work (42 citations) applies LSTM deep learning models to human gait analysis, developing real-time inverse kinematics solutions for lower-limb locomotion — a breakthrough with direct implications for prosthetics and exoskeleton design. His pioneering research on SMA-based actuators, including modular torsional actuators and a compliant rolling robot (35 citations), has advanced the field of lightweight, biomimetic actuation, culminating in a dexterous SMA-driven robotic hand modeled on human musculoskeletal anatomy. Earlier in his career, Kim made foundational contributions to wheeled modular mobile robot control, developing path manifold-based controllers and cooperative motion architectures for multi-axle compliant-frame systems — work that garnered sustained citations across robotics literature. His miniature inchworm robot (32 citations) further demonstrated his versatility in small-scale locomotion systems. Collectively, Kim's publications reflect a career dedicated to bridging biological principles with intelligent robotic design.
Research Focus
Key Achievements
Top Papers
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- 5A modular torsional actuator using shape memory alloy wires21 citations · 2015
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