The grounding of higher order concepts in action and language: A cognitive robotics model
In this paper we present a neuro-robotic model that uses artificial neural networks for investigating the relations between the development of symbol manipulation capabilities and of sensorimotor knowledge in the humanoid robot iCub. We describe a cognitive robotics model in which the linguistic input provided by the experimenter guides the autonomous organization of the robots knowledge. In this model, sequences of linguistic inputs lead to the development of higher-order concepts grounded on basic concepts and actions. In particular, we show that higher-order symbolic representations can be indirectly grounded in action primitives directly grounded in sensorimotor experiences. The use of recurrent neural network also permits the learning of higher-order concepts based on temporal sequences of action primitives. Hence, the meaning of a higher-order concept is obtained through the combination of basic sensorimotor knowledge. We argue that such a hierarchical organization of concepts can be a possible account for the acquisition of abstract words in cognitive robots.
recurrent neural network symbol grounding abstract language computational modelling developmental cognitive robotics