Learning Lexical Entries for Robotic Commands using Crowdsourcing
This work addresses the problem of enabling robots to understand flexible human commands for navigation and manipulation tasks, but it is incremental as it builds on existing translation methods.
The paper tackles the challenge of mapping syntactically diverse natural language commands into semantic concepts for robots by collecting commands via crowdsourcing and using a generative machine translation model to translate them into a defined robot language, with results showing the feasibility of this approach.
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.