A neural-dynamic architecture for flexible spatial language: Intrinsic frames, the term “between”, and autonomy

Abstract

Spatial language is a privileged channel of human-robot interaction. Here, we extend a neural-dynamic architecture for grounded spatial language in three ways. First, we introduce autonomous selection between viewer-centered and intrinsic reference frames, using an estimation of the reference object orientation to determine its intrinsic axes. Second, we employ an orientation estimation dynamics to represent the configurations of reference objects for spatial terms such as “between”. Third, we enhance the autonomy of the system so that the required sequence of attentional shifts, coordinate transforms, and selection decisions emerges from the time-continuous neural dynamics. In a robotic implementation we demonstrate how spatial language may be grounded in simple feature information obtained from video cameras and applied flexibly to dynamical scenes.