Dealing with Perceptual Deviation: Vague Semantics for Spatial Language and Quantification

Abstract

Grounding language in sensorimotor spaces is an important and difficult task. In order, for robots to be able to interpret and produce utterances about the real world, they have to link symbolic information to continuous perceptual spaces. This requires dealing with inherent vagueness, noise and differences in perspective in the perception of the real world. This paper presents two case studies for spatial language and quantification that show how cognitive operations -the building blocks of grounded procedural semantics -can be efficiently grounded in sensorimotor spaces.

Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

“…This process can fail, for instance, when a particular spatial scene does not fit the program. More precisely, evaluation can succeed, or fail, but all successful evaluations are also scored [21], as to how much the program fits the scene. Let us assume the hearer wants to interpret the example phrase and has decoded the IRL-program in Figure 5.…”

Robot perception errors and human resolution strategies in situated human–robot dialogue

“…For example, integrating world-knowledge [32] and/or linguistic ontological knowledge [3]; integrating spatial semantics into a compositional/attentional accounts of reference [23,24,31]; learning spatial semantics directly from sensor data using machine learning techniques [12,34]; modelling the functional aspects of spatial semantics in terms of predicting the dynamics of objects in the scene [10,42]; capturing the vagueness and gradation of spatial semantics [17,22,43]; and leveraging analogical reasoning mechanisms to enable agents to apply spatial semantics to new environments [13].…”

“…For example, the problem of producing referring expressions when it is not certain that the other participant shares the same perception and understanding of the scene has been addressed by [15] and [35]. Another example of research investigating language misunderstanding based on perceptual errors is [43] which examines the effect of perceptual deviation on spatial language. However, [43] deals with robot-robot dialogues and the evolution of spatial term semantics in robot populations.…”

“…Another example of research investigating language misunderstanding based on perceptual errors is [43] which examines the effect of perceptual deviation on spatial language. However, [43] deals with robot-robot dialogues and the evolution of spatial term semantics in robot populations.…”

Referential uncertainty and word learning in high-dimensional, continuous meaning spaces

“…The learner learns from the observation of the same object but from perspective of the other robot (matrix 2). GRO2 is used to evaluate what happens if there is perceptual deviation [21]. That is tutor and learner see the scene from different viewpoints and therefore have different feature estimations for objects.…”