Recognition and Representation of Robot Skills in Real Time: A Theoretical Analysis

Sharing reusable knowledge among robots has the potential to sustainably develop robot skills. The bottlenecks to sharing robot skills across a network are how to recognise and represent reusable robot skills in real-time and how to define reusable robot skills in a way that facilitates the recognition and representation challenge. In this paper, we first analyse the considerations to categorise reusable robot skills that manipulate objects derived from R.C. Schank's script representation of human basic motion, and define three types of reusable robot skills on the basis of the analysis. Then, we propose a method with potential to identify robot skills in real-time. We present a theoretical process of skills recognition during task performance. Finally, we characterise reusable robot skill based on new definitions and explain how the new proposed representation of robot skill is potentially advantageous over current state-of-the-art work.

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