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Different types of motion learning are introduced to optimize postures at each keyframe and motion curves for interpolating them, separately. Postures at every keyframe are determined by using hierarchical reinforcement learning so as to have similar features to motion samples with a data\u2010centric objective function. Plausible postures are efficiently sought among the huge number of possible states because a skeletal structure is hierarchically decomposed and postures are efficiently quantized to narrow down the configuration space. Optimized postures are then interpolated using motion curves that are learned with acceleration templates from referential motion segments. This new technique of reusing motion data is well suited to design motions by manipulating end\u2010effectors at each keyframe. \u00a9 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(5): 25\u201333, 2006; Published online in Wiley InterScience (www.interscience.wiley.com<\/jats:ext-link>). 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