Bio-inspired adaptive feedback error learning architecture for motor control.

Abstract:

:This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Tolu S,Vanegas M,Luque NR,Garrido JA,Ros E

doi

10.1007/s00422-012-0515-5

subject

Has Abstract

pub_date

2012-10-01 00:00:00

pages

507-22

issue

8-9

eissn

0340-1200

issn

1432-0770

journal_volume

106

pub_type

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