This chapter presents a formal system for representation and computation of human common-sense knowledge about the physical world to be employed in robotic natural language understanding. Mental image description language is a formal language for many-sorted predicate logic with types of terms specific to the mental image model. Mental image directed semantic theory is based on the presumption that all pieces of human knowledge of the physical world are to be reduced to attribute values of matters and their spatiotemporal relations, namely, loci in attribute spaces as mental images. A matter, usually referred to by a noun in natural language, is to be conceptualized as a conjunction of the mental images of itself and its relations with others that, in turn, are to be reduced to certain loci in attribute spaces. In mental image directed semantic theory, the atomic locus formulas, including the variable, can be logically eliminated in the case of no use or significance.