  In this work, we present the development of a neuro-inspired approach for
characterizing sensorimotor relations in robotic systems. The proposed method
has self-organizing and associative properties that enable it to autonomously
obtain these relations without any prior knowledge of either the motor (e.g.
mechanical structure) or perceptual (e.g. sensor calibration) models.
Self-organizing topographic properties are used to build both sensory and motor
maps, then the associative properties rule the stability and accuracy of the
emerging connections between these maps. Compared to previous works, our method
introduces a new varying density self-organizing map (VDSOM) that controls the
concentration of nodes in regions with large transformation errors without
affecting much the computational time. A distortion metric is measured to
achieve a self-tuning sensorimotor model that adapts to changes in either motor
or sensory models. The obtained sensorimotor maps prove to have less error than
conventional self-organizing methods and potential for further development.
