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Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

Celikkanat, H, Ohran, G, Pugeault, N, Guerin, F, Sahin, E and Kalkan, S (2015) Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation IEEE Transactions on Autonomous Mental Development.

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Abstract

In this article, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field, inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make it incremental so that (i) it does not re-learn everything from scratch given new interactions (i.e., it is online) and (ii) it can discover and add a new context into its model when necessary. We demonstrate on the iCub platform that, partly owing to modeling context on top of the concept web, our approach is adaptive, online and robust: It is adaptive and online since it can learn and discover a new context from new interactions. It is robust since it is not affected by irrelevant stimuli and it can discover contexts after a few interactions only. Moreover, we show how to use the context learned in such a model for two important tasks: object recognition and planning.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Celikkanat, HUNSPECIFIEDUNSPECIFIED
Ohran, GUNSPECIFIEDUNSPECIFIED
Pugeault, NUNSPECIFIEDUNSPECIFIED
Guerin, FUNSPECIFIEDUNSPECIFIED
Sahin, EUNSPECIFIEDUNSPECIFIED
Kalkan, SUNSPECIFIEDUNSPECIFIED
Date : 1 December 2015
Additional Information : (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 23 Sep 2015 17:56
Last Modified : 23 Sep 2015 17:56
URI: http://epubs.surrey.ac.uk/id/eprint/808484

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