Papers
3
Total Citations
12
H-Index
2
About
Hajun Kim is a robotics researcher whose work focuses on advancing legged locomotion through innovative sensor fusion and adaptive control. His primary research areas include multi-sensor state estimation, terrain-adaptive control, and hybrid model-based/learning-based frameworks for quadruped robots. Kim’s major contributions include the development of E-InEKF and E-IS frameworks, which fuse kinematics, IMU, LiDAR, and GPS data using invariant filtering and smoothing to significantly mitigate position drift in complex environments. He also proposed an online friction coefficient identification method that enables legged robots to maintain stable locomotion on slippery terrain by optimizing contact dynamics in real time. Additionally, Kim introduced a modular residual learning framework that enhances traditional model-based approaches, allowing for more robust locomotion by integrating learned residual modules with heuristic footstep planners and dynamic models. With over a dozen citations across his most-cited works published in 2025, Kim’s research is already making an impact in the robotics community, offering practical solutions for deploying legged robots in challenging, real-world conditions.
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