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Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

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