NDLI: Speed generalization capabilities of a cerebellar model on a rapid navigation task

Content Provider	IEEE Xplore Digital Library
Author	Herreros, I. Maffei, G. Brandi, S. Sanchez-Fibla, M. Verschure, P.F.M.J.
Copyright Year	2013
Description	Author affiliation: Technol. Dept., Univ. Pompeu Fabra, Barcelona, Spain (Herreros, I.; Maffei, G.; Brandi, S.; Sanchez-Fibla, M.; Verschure, P.F.M.J.)
Abstract	The cerebellum is a brain structure necessary for skilled motor behaviour and has a well understood and repetitive architecture. Such an architecture inspired the Marr-Albus-Ito theory of cerebellar learning, that provides an explanation for the acquisition of motor skills by the cerebellum. Numerous computational models inspired in such a theory have already been employed in robotic tasks. Here we look into one of the suggested roles of the cerebellum, the replacement of reflexes by anticipatory actions and we apply it to a robot navigation task. The acquisition of anticipatory actions has been thoroughly studied in the field of classical conditioning. Of particular interest is the so-called CS-intensity effect, an effect that links the rapidity of execution of an anticipatory protective action, the Conditioned Response (CR), to the intensity of a predictive signal, the Conditioning Stimulus (CS). We propose that the CS-intensity effect implements a built-in sensory-motor contingency that allows to carry over a skill learned in a safe and easy context, e.g., turning at slow velocity, to a more difficult one, e.g., a turning at a faster speed. We demonstrate this hypothesis in a series of experiments where a robot has to navigate a track that has a turn. We show that after being trained at a slow velocity, by means of the CS-intensity effect, the cerebellar controller modulates the turning such that its onset anticipates as the robot speed increases. Ultimately, through incremental learning, this generalization allows the robot to learn to navigate the track at its maximum speed.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	363
Ending Page	368
File Size	711807
Page Count	6
File Format	PDF
ISSN	21530858
DOI	10.1109/IROS.2013.6696377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Navigation Collision avoidance Brain modeling Training Computational modeling
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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