Learning and using context on a humanoid robot using latent dirichlet allocation

Hande Celikkanat; Guner Orhan; Nicolas Pugeault; Frank Guerin; Erol Sahin; Sinan Kalkan

doi:10.1109/DEVLRN.2014.6982982

Learning and using context on a humanoid robot using latent dirichlet allocation

Hande Celikkanat, Guner Orhan, Nicolas Pugeault, Frank Guerin, Erol Sahin, Sinan Kalkan

Computing Science

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

6 Citations (Scopus)

Abstract

In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts.We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.

Original language	English
Title of host publication	2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	201-207
Number of pages	7
ISBN (Electronic)	9781479975402
ISBN (Print)	9781479975419
DOIs	https://doi.org/10.1109/DEVLRN.2014.6982982
Publication status	Published - Dec 2014
Event	4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014 - Genoa, Italy Duration: 13 Oct 2014 → 16 Oct 2014

Conference

Conference	4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014
Country/Territory	Italy
City	Genoa
Period	13/10/14 → 16/10/14

Bibliographical note

We would like to thank Angelo Cangelosi, Anna Borghi
and Honghai Liu for fruitful discussions on the integrating
context in cognitive systems. This work is partially funded by
the Scientific and Technological Research Council of Turkey
(TUB¨ ˙ITAK) through project no 111E287.

Access to Document

10.1109/DEVLRN.2014.6982982Licence: Unspecified

Cite this

Celikkanat, H., Orhan, G., Pugeault, N., Guerin, F., Sahin, E., & Kalkan, S. (2014). Learning and using context on a humanoid robot using latent dirichlet allocation. In 2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob) (pp. 201-207). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEVLRN.2014.6982982

Learning and using context on a humanoid robot using latent dirichlet allocation. / Celikkanat, Hande; Orhan, Guner; Pugeault, Nicolas et al.
2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob). Institute of Electrical and Electronics Engineers Inc., 2014. p. 201-207.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Celikkanat, H, Orhan, G, Pugeault, N, Guerin, F, Sahin, E & Kalkan, S 2014, Learning and using context on a humanoid robot using latent dirichlet allocation. in 2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob). Institute of Electrical and Electronics Engineers Inc., pp. 201-207, 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014, Genoa, Italy, 13/10/14. https://doi.org/10.1109/DEVLRN.2014.6982982

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abstract = "In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts.We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.",

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AU - Kalkan, Sinan

N1 - We would like to thank Angelo Cangelosi, Anna Borghi and Honghai Liu for fruitful discussions on the integrating context in cognitive systems. This work is partially funded by the Scientific and Technological Research Council of Turkey (TUB¨ ˙ITAK) through project no 111E287.

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AB - In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts.We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.

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Learning and using context on a humanoid robot using latent dirichlet allocation

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