A self-organizing network with varying density structure for characterizing sensorimotor transformations in robotic systems

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
Number of pages12
ISBN (Print)9783030253318
Publication statusPublished - 1 Jan 2019
Event20th Towards Autonomous Robotic Systems Conference, TAROS 2019 - London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11650 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th Towards Autonomous Robotic Systems Conference, TAROS 2019
Country/TerritoryUnited Kingdom


  • Adaptive systems
  • Associative learning
  • Motor babbling
  • Robot manipulators
  • Self-organizing maps
  • Sensorimotor models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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