  High-level human instructions often correspond to behaviors with multiple
implicit steps. In order for robots to be useful in the real world, they must
be able to to reason over both motions and intermediate goals implied by human
instructions. In this work, we propose a framework for learning representations
that convert from a natural-language command to a sequence of intermediate
goals for execution on a robot. A key feature of this framework is prospection,
training an agent not just to correctly execute the prescribed command, but to
predict a horizon of consequences of an action before taking it. We demonstrate
the fidelity of plans generated by our framework when interpreting real,
crowd-sourced natural language commands for a robot in simulated scenes.
