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.2017.4.1.14How 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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license