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Following work on joint object-action representations, the functionalobject-oriented network (FOON) was introduced as a knowledge graphrepresentation for robots. Taking the form of a bipartite graph, a FOONcontains symbolic or high-level information that would be pertinent to arobot's understanding of its environment and tasks in a way that mirrors humanunderstanding of actions. In this work, we outline a road-map for futuredevelopment of FOON and its application in robotic systems for task planning aswell as knowledge acquisition from demonstration. We propose preliminary ideasto show how a FOON can be created in a real-world scenario with a robot andhuman teacher in a way that can jointly augment existing knowledge in a FOONand teach a robot the skills it needs to replicate the demonstrated actions andsolve a given manipulation problem.

A Road-map to Robot Task Execution with the Functional Object-Oriented Network