Syungkwon Ra

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We propose a framework for robot movement coordination and learning that combines elements of movement storage, dynamic models, and optimization, with the ultimate objective of efficiently generating natural, human-like motions. One of the novel features of our approach is that each movement primitive is represented and stored as a set of joint trajectory(More)
We propose an efficient and powerful alternative for adaptation of human motions to humanoid robots keeping the bipedal stability. For achieving a stable and robust whole body motion of humanoid robots, we design a biologically inspired control framework based on neural oscillators. Entrainments of neural oscillators play a key role to adapt the nervous(More)
For attaining a stable and robust dynamic bipedal locomotion, we address an efficient and powerful alternative based on biologically inspired control framework employing neural oscillators. Neural oscillators can be used to generate sustained rhythmic signals, and show superior features for stabilizing bipedal locomotion particularly when coupled with(More)
This paper proposes a new genetic operator in order to evolve the humanoid movements, which is composed of principal component analysis (PCA) and descent-based local optimization with respect to robot dynamics. The aim of the evolution is to let humanoid robots generate human-like and energy-efficient motions in real-time. We first capture human motions and(More)
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