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

doi:10.1126/sciadv.abl5068

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Sci Adv. 2021 Dec 10;7(50):eabl5068. doi: 10.1126/sciadv.abl5068. Epub 2021 Dec 10.

Authors

Imke Krauhausen^{1

2}, Dimitrios A Koutsouras¹, Armantas Melianas^{3

4}, Scott T Keene⁵, Katharina Lieberth¹, Hadrien Ledanseur¹, Rajendar Sheelamanthula⁶, Alexander Giovannitti³, Fabrizio Torricelli⁷, Iain Mcculloch^{6

8}, Paul W M Blom¹, Alberto Salleo³, Yoeri van de Burgt², Paschalis Gkoupidenis¹

Affiliations

¹ Max Planck Institute for Polymer Research, Mainz, Germany.
² Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands.
³ Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.
⁴ Exponent, 149 Commonwealth Dr, Menlo Park, CA 94025, USA.
⁵ Department of Engineering, University of Cambridge, Cambridge, UK.
⁶ KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
⁷ Department of Information Engineering, University of Brescia, 25123 Brescia, Italy.
⁸ Department of Chemistry, University of Oxford, Oxford, UK.

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.