A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot

Postnatal development in infants involves building the body schema based on integrating information from different modalities. An early phase of this complex process involves coupling proprioceptive inputs with tactile information during self-touch enabled by motor babbling. Such functionality is also desirable in humanoid robots that can serve as embodied instantiation of cognitive learning. We describe a simple connectionist model composed of neural networks that learns the proprioceptive-tactile representations on a simulated iCub humanoid robot. Input signals from both modalities – joint angles and touch stimuli on both upper limbs – are first self-organized in neural maps and then connected using a universal bidirectional associative network (UBAL). The model demonstrates the ability to predict touch and its location from proprioceptive information with relatively high accuracy. We also discuss limitations of the model and the ideas for future work.