Organic neuromorphic electronics for sensorimotor integration and learning in robotics

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 sensorimo-tor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environ-mental 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 decen-tralized sensorimotor integration.

Citation
Krauhausen, I., Koutsouras, D. A., Melianas, A., Keene, S. T., Lieberth, K., Ledanseur, H., … Gkoupidenis, P. (2021). Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Science Advances, 7(50). doi:10.1126/sciadv.abl5068

Acknowledgements
We acknowledge F. Keller, A. Steinmetz, and A. Becker from the Max Planck Institute for Polymer Research (MPIP) for significant contribution in the design and realization of the experimental setup (maze, 3D-printed parts, and video recording) and electronics (customization of the robot and additional hardware for conditioning). We also acknowledge H.-J. Guttmann and C. Bauer for assistance in the clean room facilities of MPIP. We also acknowledge G. Malliaras for relevant preliminary discussions and B. Meijer for support
Funding Facilities of MPIP (clean room, device metrology, electronics, and mechanical workshop) are supported by the Max Planck Society (to P.W.M.B. and P.G.). nano@Stanford laboratories are supported by the National Science Foundation as part of the National Nanotechnology Coordinated Infrastructure under award ECCS-1542152 (to A.S.). This study is funded by a joint project between MPIP and the Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, grant no. MPIPICMS2019001 (to Y.v.d.B., I.K., and P.G.); European Union’s Horizon 2020 Research and Innovation Programme, grant agreement no. 802615 (to Y.v.d.B.); the Carl-Zeiss Foundation (to P.G.); National Science Foundation and the Semiconductor Research Corporation, E2CDA award no. 1739795 (to A.S. and S.T.K.); and Stanford Graduate Fellowship fund (to S.T.K.)

Publisher
American Association for the Advancement of Science (AAAS)

Journal
Science Advances

DOI
10.1126/sciadv.abl5068

Additional Links
https://www.science.org/doi/10.1126/sciadv.abl5068

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