Local sensorimotor control and learning in robotics with organic neuromorphic electronics
Imke Krauhausen a b, Dimitrios Koutsouras a, Armantas Melianas c, Scott T. Keene d, Katharina Lieberth a, Hadrien Ledanseur a, Rajendar Sheelamanthula f, Alexander Giovannitti c, Fabrizio Torricelli e, Iain Mcculloch f g, Paul W. M. Blom a, Alberto Salleo c, Yoeri van de Burgt b, Paschalis Gkoupidenis a
a Max Planck Institute for Polymer Research, Mainz, Ackermannweg, 10, Mainz, Germany
b Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
c Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA;, United States
d University of Cambridge, Department of Engineering, UK, JJ Thomson Avenue, 9, Cambridge, United Kingdom
e Dept. Of Information Engineering, University of Brescia, Via Valotti, 9, Brescia, 25123, Italy
f King Abdullah University of Science and Technology (KAUST), KAUST Solar Center (KSC), Physical Sciences and Engineering Division (PSE), Material Science and Engineering Program (MSE), Thuwal 23955-6900, Kingdom of Saudi Arabia
g Department of Chemistry, University of Oxford, UK, Mansfield Rd, Oxford, United Kingdom
Proceedings of Neural Interfaces and Artificial Senses (NIAS)
Online, Spain, 2021 September 22nd - 23rd
Organizers: Tiago Costa and Georgios Spyropoulos
Oral, Imke Krauhausen, presentation 023
DOI: https://doi.org/10.29363/nanoge.nias.2021.023
Publication date: 13th September 2021

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.

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