# A Self-Organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems

## TL;DR

This paper introduces a neuro-inspired, self-organizing approach for robotic sensorimotor mapping that adapts to changes and improves accuracy without prior models, using a novel varying density self-organizing map.

## Contribution

The paper presents a new VDSOM method that enhances sensorimotor mapping accuracy and adaptability in robotic systems without prior knowledge.

## Key findings

- VDSOM reduces sensorimotor mapping errors compared to traditional methods.
- The approach adapts to changes in motor and sensory models.
- Sensorimotor maps demonstrate improved stability and accuracy.

## Abstract

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.