  Models that can execute natural language instructions for situated robotic
tasks such as assembly and navigation have several useful applications in
homes, offices, and remote scenarios. We study the semantics of
spatially-referred configuration and arrangement instructions, based on the
challenging Bisk-2016 blank-labeled block dataset. This task involves finding a
source block and moving it to the target position (mentioned via a reference
block and offset), where the blocks have no names or colors and are just
referred to via spatial location features. We present novel models for the
subtasks of source block classification and target position regression, based
on joint-loss language and spatial-world representation learning, as well as
CNN-based and dual attention models to compute the alignment between the world
blocks and the instruction phrases. For target position prediction, we compare
two inference approaches: annealed sampling via policy gradient versus
expectation inference via supervised regression. Our models achieve the new
state-of-the-art on this task, with an improvement of 47% on source block
accuracy and 22% on target position distance.
