Robotic Homunculus: Learning of Artificial Skin Representation in a Humanoid Robot Motivated by Primary Somatosensory Cortex

Abstract

Using the iCub humanoid robot with an artificial pressure-sensitive skin, we investigate how representations of the whole skin surface resembling those found in primate primary somatosensory cortex can be formed from local tactile stimulations traversing the body of the physical robot. We employ the well-known self-organizing map algorithm and introduce its modification that makes it possible to restrict the maximum receptive field (MRF) size of neuron groups at the output layer. This is motivated by findings from biology where basic somatotopy of the cortical sheet seems to be prescribed genetically and connections are localized to particular regions. We explore different settings of the MRF and the effect of activity-independent (input-output connections constraints implemented by MRF) and activity-dependent (learning from skin stimulations) mechanisms on the formation of the tactile map. The framework conveniently allows one to specify prior knowledge regarding the skin topology and thus to effectively seed a particular representation that training shapes further. Furthermore, we show that the MRF modification facilitates learning in situations when concurrent stimulation at nonadjacent places occurs (“multitouch”). The procedure was sufficiently robust and not intensive on the data collection and can be applied to any robots where representation of their “skin” is desirable.