Jaehoon Chung
Papers
3
Total Citations
50
H-Index
2
About
Jaehoon Chung is a rising researcher at the intersection of robotics and artificial intelligence, whose work is shaping how multi-agent systems navigate complex, crowded environments. His primary research areas include multi-agent pathfinding (MAPF), deep reinforcement learning (DRL), and robotic perception. Chung’s most significant contribution is his comprehensive review of DRL techniques for MAPF, which has already garnered 37 citations since its 2024 publication, establishing itself as a key reference for researchers tackling large-scale robotic coordination challenges. He has also advanced the field of robotic manipulation by developing a novel piezoelectric actuator-sensor pair for object classification on robot hands—a contribution that, with 11 citations, demonstrates his versatility in hardware-software integration. More recently, Chung has explored the critical issue of state representation in multi-agent proximal policy optimization, addressing the fundamental challenges of non-stationarity and partial observability that plague decentralized planning. His work is particularly notable for bridging the gap between theoretical reinforcement learning advances and practical robotic applications, making him a promising voice in the next generation of robotics researchers.
Research Focus
Key Achievements
Top Papers
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