  We build upon the functional object-oriented network (FOON), a structured
knowledge representation which is constructed from observations of human
activities and manipulations. A FOON can be used for representing object-motion
affordances. Knowledge retrieval through graph search allows us to obtain novel
manipulation sequences using knowledge spanning across many video sources,
hence the novelty in our approach. However, we are limited to the sources
collected. To further improve the performance of knowledge retrieval as a
follow up to our previous work, we discuss generalizing knowledge to be applied
to objects which are similar to what we have in FOON without manually
annotating new sources of knowledge. We discuss two means of generalization: 1)
expanding our network through the use of object similarity to create new
functional units from those we already have, and 2) compressing the functional
units by object categories rather than specific objects. We discuss experiments
which compare the performance of our knowledge retrieval algorithm with both
expansion and compression by categories.
