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Learning inverse kinematics using neural computational primitives on neuromorphic hardware

Engineering and Technology

Learning inverse kinematics using neural computational primitives on neuromorphic hardware

J. Zhao, M. Monforte, et al.

This research showcases a groundbreaking online motor control system powered by a hardware spiking neural network (SNN). Conducted by Jingyue Zhao, Marco Monforte, Giacomo Indiveri, Chiara Bartolozzi, and Elisa Donati, the SNN achieves an impressive 97.93% accuracy in learning the inverse kinematics of a robotic arm, paving the way for neuromorphic computing in real-world applications.... show more
Citation Metrics
Citations
17
Influential Citations
0
Reference Count
26
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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