Ga-Ram Park
Papers
2
Total Citations
27
H-Index
2
About
Ga-Ram Park is a robotics researcher whose work bridges imitation learning and evolutionary computation to create more natural, human-like motion in humanoid robots. His primary research areas include humanoid robotics, evolutionary algorithms, and motion generation systems. Park’s most significant contribution is the development of a framework that uses Evolutionary Algorithm (EA)-based imitation learning to generate human-like arm motions for humanoid robots in real time. This approach, detailed in his highly cited 2009 paper (22 citations), involves two key processes: first, learning human arm motions through imitation, and second, constructing an optimal motion database that allows the robot to generate appropriate, natural-seeming arm movements on the fly. His follow-up work (2010, 5 citations) further refined this by focusing on the efficient construction of that database structure. By combining the adaptability of evolutionary algorithms with the intuitiveness of imitation learning, Park has addressed a core challenge in robotics: enabling machines to move with the fluidity and grace of humans, rather than with stiff, pre-programmed motions. His work has laid important groundwork for more responsive and natural human-robot interaction.
Research Focus
Key Achievements
Top Papers
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