TY - JOUR
T1 - Organic neuromorphic electronics for sensorimotor integration and learning in robotics
AU - Krauhausen, Imke
AU - Koutsouras, Dimitrios A.
AU - Melianas, Armantas
AU - Keene, S. T.
AU - Lieberth, Katharina
AU - Ledanseur, Hadrien
AU - Sheelamanthula, Rajendar
AU - Giovannitti, Alexander
AU - Torricelli, Fabrizio
AU - McCulloch, Iain
AU - Blom, Paul W. M.
AU - Salleo, Alberto
AU - van de Burgt, Yoeri
AU - Gkoupidenis, Paschalis
N1 - KAUST Repository Item: Exported on 2021-12-13
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
PY - 2021/12/10
Y1 - 2021/12/10
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/673985
UR - https://www.science.org/doi/10.1126/sciadv.abl5068
U2 - 10.1126/sciadv.abl5068
DO - 10.1126/sciadv.abl5068
M3 - Article
C2 - 34890232
SN - 2375-2548
VL - 7
JO - Science advances
JF - Science advances
IS - 50
ER -