Abstract
In this work we have implemented and tested a framework for training and analyzing auto-encoding architectures for the purpose of encoding state space representations into latent space, and thus compressing the essential space properties depicted in the training image dataset. We explored the possibility of incorporating a forward model in between encoder and decoder to predict the next state in series of temporally arranged images, with the aim of testing if it is able to predict meaningful representations (even if they are not interpretable by visual inspection). The application domain of this approach is that of cognitive robotics, where having a state representation learning system that can operate in an open-ended fashion is necessary. State representation learning has been a challenging topic in autonomous robotics for a long time, and some promising approaches have been developed. Anyway, when applying them to open-ended learning, as is the case of cognitive robotics, current solutions fail, so new systems must be implemented. The work presented here is a first approach towards this objective.
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Acknowledgements
This work has been partially funded by the Ministerio de Ciencia, Innovación y Universidades of Spain/FEDER (grant RTI2018-101114-B-I00) and Xunta de Galicia and FEDER (grant ED431C 2017/12).
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Meden, B., Prieto, A., Peer, P., Bellas, F. (2020). First Steps Towards State Representation Learning for Cognitive Robotics. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_41
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