  We present a unified framework which supports grounding natural-language
semantics in robotic driving. This framework supports acquisition (learning
grounded meanings of nouns and prepositions from human annotation of robotic
driving paths), generation (using such acquired meanings to generate sentential
description of new robotic driving paths), and comprehension (using such
acquired meanings to support automated driving to accomplish navigational goals
specified in natural language). We evaluate the performance of these three
tasks by having independent human judges rate the semantic fidelity of the
sentences associated with paths, achieving overall average correctness of 94.6%
and overall average completeness of 85.6%.
