A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

DOI

10.2991/jrnal.2017.4.1.14How to use a DOI?

Keywords

Motor control, cerebellum, machine learning, modular robot, internal model, adaptive behavior.

Abstract

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Learning Machine. The LWPR algorithm optimizes the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ismael Baira Ojeda
AU  - Silvia Tolu
AU  - Moisés Pacheco
AU  - David Johan Christensen
AU  - Henrik Hautop Lund
PY  - 2017
DA  - 2017/06/01
TI  - A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 62
EP  - 66
VL  - 4
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.1.14
DO  - 10.2991/jrnal.2017.4.1.14
ID  - Ojeda2017
ER  -