  Robots can complete all human-performed tasks, but due to their current lack
of knowledge, some tasks still cannot be completed by them with a high degree
of success. However, with the right knowledge, these tasks can be completed by
robots with a high degree of success, reducing the amount of human effort
required to complete daily tasks. In this paper, the FOON, which describes the
robot action success rate, is discussed. The functional object-oriented network
(FOON) is a knowledge representation for symbolic task planning that takes the
shape of a graph. It is to demonstrate the adaptability of FOON in developing a
novel and adaptive method of solving a problem utilizing knowledge obtained
from various sources, a graph retrieval methodology is shown to produce
manipulation motion sequences from the FOON to accomplish a desired aim. The
outcomes are illustrated using motion sequences created by the FOON to complete
the desired objectives in a simulated environment.
