Using semantic fields to model dynamic spatial relations in a robot architecture for natural language instruction of service robots

Abstract

We present a methodology for enabling service robots to follow natural language commands from non-expert users, with and without user-specified constraints, with a particular focus on spatial language understanding. As part of our approach, we propose a novel extension to the semantic field model of spatial prepositions that enables the representation of dynamic spatial relations involving paths. The design, system modules, and implementation details of our robot software architecture are presented and the relevance of the proposed methodology to interactive instruction and task modification through the addition of constraints is discussed. The paper concludes with an evaluation of our robot software architecture implemented on a simulated mobile robot operating in both a 2D home environment and in real world environment maps to demonstrate the generalizability and usefulness of our approach in real world applications.