  Human infants are able to acquire natural language seemingly easily at an
early age. Their language learning seems to occur simultaneously with learning
other cognitive functions as well as with playful interactions with the
environment and caregivers. From a neuroscientific perspective, natural
language is embodied, grounded in most, if not all, sensory and sensorimotor
modalities, and acquired by means of crossmodal integration. However,
characterising the underlying mechanisms in the brain is difficult and
explaining the grounding of language in crossmodal perception and action
remains challenging. In this paper, we present a neurocognitive model for
language grounding which reflects bio-inspired mechanisms such as an implicit
adaptation of timescales as well as end-to-end multimodal abstraction. It
addresses developmental robotic interaction and extends its learning
capabilities using larger-scale knowledge-based data. In our scenario, we
utilise the humanoid robot NICO in obtaining the EMIL data collection, in which
the cognitive robot interacts with objects in a children's playground
environment while receiving linguistic labels from a caregiver. The model
analysis shows that crossmodally integrated representations are sufficient for
acquiring language merely from sensory input through interaction with objects
in an environment. The representations self-organise hierarchically and embed
temporal and spatial information through composition and decomposition. This
model can also provide the basis for further crossmodal integration of
perceptually grounded cognitive representations.
