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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Discovering the Relationship Between the Morphology and the Internal Model in a Robot System by Means of Neural Networks
verfasst von : Angel J. Duran, Angel P. del Pobil
Erschienen in: Intelligent Autonomous Systems 14
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Abstract
Supervised machine learning techniques have proven very effective to solve the problems arising from model learning in robotics. A significant limitation of such approaches is that internal models learned for a specific robot are likely to fail when transferred to a robot with a different morphology. One of the challenges to relate the morphology and the internal model is the difference in the number of parameters that define them. We propose three neural network architectures for solving this problem, along with a case study to evaluate their performance, namely saccadic movements in a robotic head. We generate a huge dataset to test the performance of the proposed architectures. Our results suggest that the best solution is provided by the parallel neural network, due to the fact that the trained weights are independent of one another.