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Robotic commands in natural language usually contain various spatialdescriptions that are semantically similar but syntactically different. Mappingsuch syntactic variants into semantic concepts that can be understood by robotsis challenging due to the high flexibility of natural language expressions. Totackle this problem, we collect robotic commands for navigation andmanipulation tasks using crowdsourcing. We further define a robot language anduse a generative machine translation model to translate robotic commands fromnatural language to robot language. The main purpose of this paper is tosimulate the interaction process between human and robots using crowdsourcingplatforms, and investigate the possibility of translating natural language torobot language with paraphrases.

Learning Lexical Entries for Robotic Commands using Crowdsourcing