  We study the problem of learning a robot policy to follow natural language
instructions that can be easily extended to reason about new objects. We
introduce a few-shot language-conditioned object grounding method trained from
augmented reality data that uses exemplars to identify objects and align them
to their mentions in instructions. We present a learned map representation that
encodes object locations and their instructed use, and construct it from our
few-shot grounding output. We integrate this mapping approach into an
instruction-following policy, thereby allowing it to reason about previously
unseen objects at test-time by simply adding exemplars. We evaluate on the task
of learning to map raw observations and instructions to continuous control of a
physical quadcopter. Our approach significantly outperforms the prior state of
the art in the presence of new objects, even when the prior approach observes
all objects during training.
