Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas, Scott T. Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar Sheelamanthula, Alexander Giovannitti, Fabrizio Torricelli, Iain McCulloch, Paul W.M. Blom, Alberto Salleo, Yoeri van de Burgt, Paschalis Gkoupidenis

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Original languageEnglish (US)
Article numbereabl5068
JournalScience Advances
Volume7
Issue number50
DOIs
StatePublished - Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

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