TY - GEN
T1 - Learning of labeling room space for mobile robots based on visual motor experience
AU - Yamada, Tatsuro
AU - Ito, Saki
AU - Arie, Hiroaki
AU - Ogata, Tetsuya
N1 - Funding Information:
Acknowledgments. This work was supported by JSPS Grant-in-Aid for Young Scientists (A) (No. 16H05878), and JST CREST Grant Number: JPMJCR15E3.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Deep autoencoder
KW - Indoor scene labeling
KW - Mobile robots
KW - Recurrent neural network
KW - Symbol grounding
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U2 - 10.1007/978-3-319-68600-4_5
DO - 10.1007/978-3-319-68600-4_5
M3 - Conference contribution
AN - SCOPUS:85034244147
SN - 9783319685991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 35
EP - 42
BT - Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
A2 - Verschure, Paul F.
A2 - Lintas, Alessandra
A2 - Villa, Alessandro E.
A2 - Rovetta, Stefano
PB - Springer Verlag
T2 - 26th International Conference on Artificial Neural Networks, ICANN 2017
Y2 - 11 September 2017 through 14 September 2017
ER -