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Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi

Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One probl...

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Detalles Bibliográficos
Autores principales: Michaelis, Carlo, Lehr, Andrew B., Tetzlaff, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726255/
https://www.ncbi.nlm.nih.gov/pubmed/33324191
http://dx.doi.org/10.3389/fnbot.2020.589532
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author Michaelis, Carlo
Lehr, Andrew B.
Tetzlaff, Christian
author_facet Michaelis, Carlo
Lehr, Andrew B.
Tetzlaff, Christian
author_sort Michaelis, Carlo
collection PubMed
description Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.
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spelling pubmed-77262552020-12-14 Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi Michaelis, Carlo Lehr, Andrew B. Tetzlaff, Christian Front Neurorobot Neuroscience Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware. Frontiers Media S.A. 2020-11-26 /pmc/articles/PMC7726255/ /pubmed/33324191 http://dx.doi.org/10.3389/fnbot.2020.589532 Text en Copyright © 2020 Michaelis, Lehr and Tetzlaff. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Michaelis, Carlo
Lehr, Andrew B.
Tetzlaff, Christian
Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title_full Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title_fullStr Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title_full_unstemmed Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title_short Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
title_sort robust trajectory generation for robotic control on the neuromorphic research chip loihi
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726255/
https://www.ncbi.nlm.nih.gov/pubmed/33324191
http://dx.doi.org/10.3389/fnbot.2020.589532
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