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Straight Swimming Algorithm Used by a Design of Biomimetic Robotic Fish Read chapter Read first chapter

2017 | OriginalPaper | Chapter

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

Authors : Ismael Baira Ojeda, Silvia Tolu, Henrik H. Lund

Published in: Biomimetic and Biohybrid Systems

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-Like Network
Authors
Ismael Baira Ojeda
Silvia Tolu
Henrik H. Lund
Copyright Year
2017
Book
Biomimetic and Biohybrid Systems

Print ISBN: 978-3-319-63536-1

Electronic ISBN: 978-3-319-63537-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63537-8_31

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