Orienting learning by exploiting sociality: an evolutionary robotics simulation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A cross-situational algorithm for learning a lexicon using neural modeling fields
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The facilitatory role of linguistic instructions on developing manipulation skills
IEEE Computational Intelligence Magazine
Language and cognition interaction neural mechanisms
Computational Intelligence and Neuroscience
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In this paper, we present some recent cognitive robotics studies on language and cognition integration to demonstrate how the language acquired by robotic agents can be directly grounded in action representations. These studies are characterized by the hypothesis that symbols are directly grounded into the agents' own categorical representations, while at the same time having logical (e.g. syntactic) relationships with other symbols. The two robotics studies are based on the combination of cognitive robotics with neural modeling methodologies, such as connectionist models and modeling field theory. Simulations demonstrate the efficacy of the mechanisms of action grounding of language and the symbol grounding transfer in agents that acquire lexicon via imitation and linguistic instructions. The paper also discusses the scientific and technological implications of such an approach.