A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

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Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input-driven contributions to deliver accurate corrective commands to the global IM. This article extends the earlier work by including the Deep Cerebellar Nuclei (DCN) and by reproducing the Purkinje and the DCN layers using a spiking neural network (SNN) implemented on the neuromorphic SpiNNaker platform. The performance and robustness outcomes from the real robot tests are promising for neural control scalability.
Original languageEnglish
Title of host publicationProceedings of Living Machines 2017
PublisherSpringer
Publication date2017
Pages375-386
DOIs
StatePublished - 2017
EventLiving Machines 2017 - Stanford, United States

Conference

ConferenceLiving Machines 2017
LocationStanford University
CountryUnited States
CityStanford
Period25/07/201728/07/2017
SeriesLecture Notes in Computer Science
Volume10384
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Neuro-robotics, Bio-inspiration, Motor control, Cerebellum, Machine Learning, Compliant control, Internal model
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