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About

Eunsu Kim is a robotics researcher whose work centers on bipedal locomotion, specifically the complex challenge of enabling humanoid robots to navigate stairs. Kim's major contributions lie in the development of optimal trajectory generation schemes for ascending and descending staircases, a critical capability for practical humanoid deployment in human-centric environments. By pioneering the use of the univariate dynamic encoding algorithm for searches (uDEAS) and genetic algorithms (GA), Kim has advanced the precision and efficiency of bipedal gait planning. In a seminal 2009 paper (8 citations), Kim simulated and implemented a humanoid walking up and down stairs using blending polynomials and uDEAS, dividing efficient walking steps for a commercial robot. A subsequent 2010 study (12 citations) further refined these trajectory generation schemes. Kim's work is notable for proposing and simulating four distinct walking schemes for stair ascent, leveraging recently developed biped kinematics. With a cumulative impact of over 30 citations across these foundational studies, Kim's research provides essential methodologies for stable, optimal stair navigation, directly contributing to the broader goal of integrating humanoid robots into multi-level environments.