Understanding Natural Language in Context

TL;DR

This paper explores translating natural language commands into formal robot knowledge representations to enable complex household tasks, leveraging language models, planning tools, and contextual analysis.

Contribution

It introduces a method combining language models, planning, and context to improve natural language understanding for cognitive robots in household environments.

Findings

Effective translation of natural language to robot formalism

Enhanced understanding of directive types and context impact

Improved communication for household task execution

Abstract

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.