Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well...

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Detalles Bibliográficos
Autores principales: Volinski, Alex, Zaidel, Yuval, Shalumov, Albert, DeWolf, Travis, Supic, Lazar, Ezra Tsur, Elishai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767299/
https://www.ncbi.nlm.nih.gov/pubmed/35079712
http://dx.doi.org/10.1016/j.patter.2021.100391

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767299/
https://www.ncbi.nlm.nih.gov/pubmed/35079712
http://dx.doi.org/10.1016/j.patter.2021.100391

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