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It has been observed that human limb motions are not very accurate, leading to the hypothesis that the human motor control system may have simplified motion commands at the expense of motion accuracy. Inspired by this hypothesis, we propose a learning scheme that trades motion accuracy for motion command simplification. When the original complex motion commands capable of accurate motion tracking are simplified into those in simple forms, the simplified motion commands can that be stored and manipulated by using learning mechanisms with simple structures and scanty memory resources, and they can be fast and smoothly executed. In addition, this learning scheme can also perform motion command scaling, so that simplified motion commands be provided for a number of similar motions of different movement distances and velocities without system dynamics re-calculation. Experiments based on robot motions are reported that demonstrate the effectiveness of the proposed learning scheme in implementing this accuracy-simplification tradeoff.
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