A modular neural network linking Hyper RBF and AVITE models for reaching moving objects

Authors:
J. L. Pedreñ/o-molina;J. Molina-vilaplana;J. Ló/pez-coronado;P. Gorce
Affiliations:
Department of Information Technologies and Communications;Department of Systems Engineering and Automation Technical University of Cartagena. Campus Muralla del Mar,s7sol/n. 30.202 Cartagena (SPAIN);Department of Systems Engineering and Automation Technical University of Cartagena. Campus Muralla del Mar,s7sol/n. 30.202 Cartagena (SPAIN);Université/ du Sud Toulon-Var, LESP EA 31-62, Avenue de l'Université/, 83957 La Garde (France)
Venue:
Robotica
Year:
2005

Citing 5
Cited 1

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A modular neural network architecture for step-wise learning of grasping tasks

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Abstract

In this paper, the problem of precision reaching applications in robotic systems for scenarios with static and non-static objects has been considered and a solution based on a modular neural architecture has been proposed and implemented. The goal of this solution is to combine robustness and capability mapping trajectories from two biologically plausible neural network sub-modules: Hyper RBF and AVITE. The Hyper Basis Radial Function (HypRBF) neural network solves the inverse kinematic in redundant robotic systems, while the Adaptive Vector Integration to End-Point (AVITE) visuo-motor neural model quickly maps the difference vector between current and desired position in both spatial (visual information) and motor coordinates (propioceptive information). The anthropomorphic behaviour of the proposed architecture for reaching and tracking tasks in presence of spatial perturbations has been validated over a real arm-head robotic platform.