Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm

Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics....

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Bibliographic Details
Published inarXiv.org
Main Authors Linares-Barranco, A, Pinero-Fuentes, E, Canas-Moreno, S, Rios-Navarro, A, Maryada, Wu, Chenxi, Zhao, Jingyue, Zendrikov, D, Indiveri, G
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.02.2022
Subjects
Circuits
Controllers
Hardware
Limbs
Microprocessors
Neural networks
Robot arms
Robot control
Robotics
Spiking
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Summary:Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA circuit implemented in a Spiking-Neural-Network (SNN) processor to produce such patterns for controlling a robotic arm in real-time. The robot uses spike-based proportional-integrativederivative (SPID) controllers to keep a commanded joint position from the winner population of neurons of the WTA circuit. Experiments demonstrate the feasibility of robotic control with spiking circuits following brain-inspiration.
ISSN:2331-8422