Abstract
A model was developed to allow a mobile robot to label the areas of a typical domestic room, using raw sequential visual and motor data, no explicit information on location was provided, and no maps were constructed. The model comprised a deep autoencoder and a recurrent neural network. The model was demonstrated to (1) learn to correctly label areas of different shapes and sizes, (2) be capable of adapting to changes in room shape and rearrangement of items in the room, and (3) attribute different labels to the same area, when approached from different angles. Analysis of the internal representations of the model showed that a topological structure corresponding to the room structure was self-organized as the trajectory of the internal activations of the network.
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Acknowledgments
This work was supported by JSPS Grant-in-Aid for Young Scientists (A) (No. 16H05878), and JST CREST Grant Number: JPMJCR15E3.
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Yamada, T., Ito, S., Arie, H., Ogata, T. (2017). Learning of Labeling Room Space for Mobile Robots Based on Visual Motor Experience. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_5
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DOI: https://doi.org/10.1007/978-3-319-68600-4_5
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