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The paper presents a neurorobotics cognitive model to explain theunderstanding and generalisation of nouns and verbs combinations when a vocalcommand consisting of a verb-noun sentence is provided to a humanoid robot.This generalisation process is done via the grounding process: differentobjects are being interacted, and associated, with different motor behaviours,following a learning approach inspired by developmental language acquisition ininfants. This cognitive model is based on Multiple Time-scale Recurrent NeuralNetworks (MTRNN).With the data obtained from object manipulation tasks with ahumanoid robot platform, the robotic agent implemented with this model canground the primitive embodied structure of verbs through training withverb-noun combination samples. Moreover, we show that a functional hierarchicalarchitecture, based on MTRNN, is able to generalise and produce novelcombinations of noun-verb sentences. Further analyses of the learned networkdynamics and representations also demonstrate how the generalisation ispossible via the exploitation of this functional hierarchical recurrentnetwork.

Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics  Model for Generation and Generalisation of Noun-Verb Combinations with  Sensorimotor Inputs