Abstract
The purpose of this paper is to exhibit the process of a Growing Functional Modules (GFM) controller designed for a humanoid robot balance learning. This learning based controller is graphically generated by interconnecting and configuring four kinds of components: Global Goals, Acting Modules, Sensing Modules and Sensations. Global Goals specify the intrinsic motivations of the controller. Acting and Sensing Modules develop their acting and respectively, sensing functionalities while interacting with the environment. Sensations provide the controlled system’s feedback that renders the effects produced by the previous command. These characteristics together with an endless learning process allow the controller to perform as an ‘artificial brain’. The present paper describes the design, functioning and performance of a humanoid equilibrium subsystem that learns balancing the robot on one foot meanwhile a disequilibrium is artificially produced by moving the opposite leg.
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Leboeuf-Pasquier, J. (2015). A Growing Functional Modules Learning Based Controller Designed to Balance of a Humanoid Robot on One Foot. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_26
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DOI: https://doi.org/10.1007/978-3-319-18833-1_26
Publisher Name: Springer, Cham
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