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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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