  The paper presents a neurorobotics cognitive model to explain the
understanding and generalisation of nouns and verbs combinations when a vocal
command consisting of a verb-noun sentence is provided to a humanoid robot.
This generalisation process is done via the grounding process: different
objects are being interacted, and associated, with different motor behaviours,
following a learning approach inspired by developmental language acquisition in
infants. This cognitive model is based on Multiple Time-scale Recurrent Neural
Networks (MTRNN).With the data obtained from object manipulation tasks with a
humanoid robot platform, the robotic agent implemented with this model can
ground the primitive embodied structure of verbs through training with
verb-noun combination samples. Moreover, we show that a functional hierarchical
architecture, based on MTRNN, is able to generalise and produce novel
combinations of noun-verb sentences. Further analyses of the learned network
dynamics and representations also demonstrate how the generalisation is
possible via the exploitation of this functional hierarchical recurrent
network.
