  Recent years have seen an increasing number of applications that have a
natural language interface, either in the form of chatbots or via personal
assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana
(Microsoft). To use these applications, a basic dialog between the robot and
the human is required.
  While this kind of dialog exists today mainly within "static" robots that do
not make any movement in the household space, the challenge of reasoning about
the information conveyed by the environment increases significantly when
dealing with robots that can move and manipulate objects in our home
environment.
  In this paper, we focus on cognitive robots, which have some knowledge-based
models of the world and operate by reasoning and planning with this model.
Thus, when the robot and the human communicate, there is already some formalism
they can use - the robot's knowledge representation formalism.
  Our goal in this research is to translate natural language utterances into
this robot's formalism, allowing much more complicated household tasks to be
completed. We do so by combining off-the-shelf SOTA language models, planning
tools, and the robot's knowledge-base for better communication. In addition, we
analyze different directive types and illustrate the contribution of the
world's context to the translation process.
