  Robotic commands in natural language usually contain various spatial
descriptions that are semantically similar but syntactically different. Mapping
such syntactic variants into semantic concepts that can be understood by robots
is challenging due to the high flexibility of natural language expressions. To
tackle this problem, we collect robotic commands for navigation and
manipulation tasks using crowdsourcing. We further define a robot language and
use a generative machine translation model to translate robotic commands from
natural language to robot language. The main purpose of this paper is to
simulate the interaction process between human and robots using crowdsourcing
platforms, and investigate the possibility of translating natural language to
robot language with paraphrases.
