The iCub learns numbers: An embodied cognition study

Thanks to recent technological advances and the increasing interest towards the Cognitive Developmental Robotics (CDR) paradigm, many popular platforms for scientific research have been designed in order to resemble the shape of the human body. The motivation behind this strongly humanoid design is the embodied cognition hypothesis, which affirms that all aspects of cognition are shaped by aspects of the body. Thus CDR is based on a synthetic approach that aims to provide new understanding on how human beings develop their higher cognitive functions. Following this paradigm we have developed an artificial model, based on artificial neural networks, to explore finger counting and the association of number words (or tags) to the fingers, as bootstrapping for the representation of numbers in the humanoid robot iCub. In this paper, we detail experiments of our model with the iCub robotic platform. Results of the number learning with propri-oceptive data from the real platform are reported and compared with the ones obtained instead, with the simulated platform. These results support the thesis that learning the number words in sequence, along with finger configurations helps the building of the initial representation of number in the robot. Moreover, the comparison between the real and simulated iCub gives insights on the use of these platforms as a tool for CDR.