Heeseon Hwang

Korea Institute of Robot and Convergence, Pohang University of Science and Technology

Papers

Total Citations

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About

Heeseon Hwang is a robotics researcher whose work spans neuromorphic computing, legged locomotion, and motion planning for robotic systems. Drawing on expertise at the intersection of sensory processing and autonomous movement, Hwang has made notable contributions to two distinct but complementary domains within robotics. In the realm of tactile sensing, his 2020 study on object shape recognition using spiking neural networks with unsupervised learning (11 citations) demonstrated a promising pathway for enabling robots to process tactile information in a biologically inspired manner — a capability essential for dexterous robotic manipulation. His earlier work on quadruped locomotion produced influential gait generation algorithms for walking robots operating on irregular terrain without precise terrain data, with his steady crawl gait paper earning 10 citations and helping advance blind quadruped navigation. Complementing this, his research on dynamic crawl gait adaptation and locomotion via impact switching between decoupling vector fields addressed fundamental challenges in underactuated robotic systems and motion planning. Together, Hwang's body of work reflects a sustained commitment to building more capable, adaptable robots — from how they sense their environment to how they move through it.