A Deep Incremental Boltzmann Machine for Modeling Context in Robots

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2018-05-25

Author

Doğan, Fethiye Irmak
Çelikkanat, Hande
Kalkan, Sinan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.

Subject Keywords

Neurons , Data models, Adaptation models, Computational modeling, Robots, Hidden Markov models, Context modeling

URI

https://hdl.handle.net/11511/37700

DOI

https://doi.org/10.1109/icra.2018.8462925

Conference Name

IEEE International Conference on Robotics and Automation (ICRA)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

F. I. Doğan, H. Çelikkanat, and S. Kalkan, “A Deep Incremental Boltzmann Machine for Modeling Context in Robots,” presented at the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, AUSTRALIA, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37700.