Local sensorimotor control and learning in robotics with organic neuromorphic electronics

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Artificial intelligence applications have demonstrated their enormous potential for complex processing over the last decade, however they still lack the efficiency and computing capacity of the brain. In living organisms, data signals are represented by sensory and motor processes that are distributed, locally merged and capable of forming dynamic sensorimotor associations through volatile and non-volatile connections. Using similar computational primitives, neuromorphic circuits offer a new way of intelligent information processing that makes it possible to adaptively oberserve, anaylze, operate and interact in real-world scenarios [1-6]. In this work we present a small-scale, locally-trained organic neuromorphic circuit for sensorimotor control and learning, on a robot navigating inside a maze. By connecting the neuromorphic circuit directly to environmental stimuli through sensor signals, the robot is able to respond adaptively to sensory cues and consequently forms a behavioral association to follow the way to the exit. The on-chip sensorimotor integration with low-voltage organic neuromorphic electronics opens the way towards stand-alone, brain-inspired circuitry in autonomous and intelligent robotics.
Original languageEnglish (US)
Title of host publicationProceedings of the Neural Interfaces and Artificial Senses
PublisherFundació Scito
StatePublished - Sep 13 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-28


Dive into the research topics of 'Local sensorimotor control and learning in robotics with organic neuromorphic electronics'. Together they form a unique fingerprint.

Cite this