  Developmental psychology and neuroimaging research identified a close link
between numbers and fingers, which can boost the initial number knowledge in
children. Recent evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article explores the
application of embodied strategies to convolutional neural network models in
the context of developmental neuro-robotics, where the training information is
likely to be gradually acquired while operating rather than being abundant and
fully available as the classical machine learning scenarios. The experimental
analyses show that the proprioceptive information from the robot fingers can
improve network accuracy in the recognition of handwritten Arabic digits when
training examples and epochs are few. This result is comparable to brain
imaging and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case of artificial
agents' training and show a possible way for the humanization of the learning
process, where the robotic body can express the internal processes of
artificial intelligence making it more understandable for humans.
