Understanding Natural Language in Context

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 assistant 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. In this paper we describe an approach for translating natural language directives into the robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SoTA large language models, planning tools, and the robot knowledge of the state of the world and of its own model. This results in much more accurate interpretation of directives in natural language.