Authors
Ismael Baira Ojeda, Silvia Tolu, Moisés Pacheco, David Johan Christensen,
Henrik Hautop Lund
Corresponding Author
Ismael Baira Ojeda
Available Online 1 June 2017.
DOI
https://doi.org/10.2991/jrnal.2017.4.1.14
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license