  Natural language instruction following tasks serve as a valuable test-bed for
grounded language and robotics research. However, data collection for these
tasks is expensive and end-to-end approaches suffer from data inefficiency. We
propose the structuring of language, acting, and visual tasks into separate
modules that can be trained independently. Using a Language, Action, and Vision
(LAV) framework removes the dependence of action and vision modules on
instruction following datasets, making them more efficient to train. We also
present a preliminary evaluation of LAV on the ALFRED task for visual and
interactive instruction following.
