Interacting with a Robot to Enhance Its Perceptual Attention

Abstract

Robots are like humans in that they cannot and do not pay attention all the time; they do not process all the information perceived, and make decisions about what is worth their attention. If we put a robot in a social environment (either with other robots, or humans), this can help it analyse perceptual information: modelling attention is very hard, and a social context simplifies the problem. The purpose of this paper is to examine the effect of interacting with a robot on the characteristics of the data it is exposed to. We want to show that the perceptual data is better structured when the robot is in a social situation. We analyse the data using Principal Component Analysis, which is a multivariate data-analysis tool. We do not actually present here a computational attention algorithm for analysing perceptual data, although we will mention the system we are currently developing, which is based on the Self Organising Feature Map.

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