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• W2896612851 abstract "For a robot, the ability to adapt his knowledge automatically and customize its behavior is a key feature. Furthermore, a robot should be able to carry out its tasks at a long-term basis, performing it seamlessly in presence of changes in their surroundings. To do that, it is essential that the robot dynamically learn from their environment, but to perform a fully retraining of a deep learning architecture when the model needs new knowledge is a highly time consuming task. This work focus on exploring several strategies to include new data to an already learned model, applied to the semantic localization problem focusing in the accuracy of the final model and their training time. Exhaustive experimentation is carried out and each result is discussed consequently." @default.
• W2896612851 created "2018-10-26" @default.
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• W2896612851 date "2018-07-01" @default.
• W2896612851 modified "2023-09-25" @default.
• W2896612851 title "Finding the Place: How to Train and Use Convolutional Neural Networks for a Dynamically Learning Robot" @default.
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• W2896612851 doi "https://doi.org/10.1109/ijcnn.2018.8489469" @default.
• W2896612851 hasPublicationYear "2018" @default.
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