  The speed and accuracy with which robots are able to interpret natural
language is fundamental to realizing effective human-robot interaction. A great
deal of attention has been paid to developing models and approximate inference
algorithms that improve the efficiency of language understanding. However,
existing methods still attempt to reason over a representation of the
environment that is flat and unnecessarily detailed, which limits scalability.
An open problem is then to develop methods capable of producing the most
compact environment model sufficient for accurate and efficient natural
language understanding. We propose a model that leverages environment-related
information encoded within instructions to identify the subset of observations
and perceptual classifiers necessary to perceive a succinct,
instruction-specific environment representation. The framework uses three
probabilistic graphical models trained from a corpus of annotated instructions
to infer salient scene semantics, perceptual classifiers, and grounded symbols.
Experimental results on two robots operating in different environments
demonstrate that by exploiting the content and the structure of the
instructions, our method learns compact environment representations that
significantly improve the efficiency of natural language symbol grounding.
