ROBOTS ACQUIRING TASKS FROM EXAMPLES

Abstract

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.