Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Xiaogang Ruan, Jing Chen, Lizhen Dai
DOI: 10.4236/ica.2011.23026                

Abstract

A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.

Keywords

Motor Learning, Cerebellum, Basal Ganglia, Operant Learning, Self-Balancing Two-Wheeled Robot

Conflicts of Interest

The authors declare no conflicts of interest.

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