Embodied Attention in Word-Object Mapping: A Developmental Cognitive Robotics Model

Abstract

Developmental Robotics models provide useful tools to study and understand the language learning process in infants and robots. These models allow us to describe key mechanisms of language development, such as statistical learning, the role of embodiment, and the impact of the attention payed to an object while learning its name. Robots can be particularly well suited for this type of problems, because they cover both a physical manipulation of the environment and mathematical modeling of the temporal changes of the learned concepts. In this work we present a computational representation of the impact of embodiment and attention on word learning, relying on sensory data collected with a real robotic agent in a real world scenario. Results show that the cognitive architecture designed for this scenario is able to capture the changes underlying the moving object in the field of view of the robot. The architecture successfully handles the temporal relationship in moving items and manages to show the effects of the embodied attention on word-object mapping.