This paper describes a task acquisition system which is being
implemented on a six-joint robot. Functions controlling the
robot are constructed directly from examples of the user leading
it. The numerical robot feedback is passed through a symbolic
processing stage to convert it into primitive motion functions.
Thereafter, generalization occurs at two levels - the primitive
motion function names and the arguments to these primitive
functions. The constructed task function may contain loops, conditionals,
and variables. All variables are determined from the objects which are
manipulated. General algorithms are described, examples are given, and
comparisons to existing operator learning systems are presented.
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