A Probabilistic Concept Web on a Humanoid Robot

Çelikkanat, Hande
Orhan, Guner
Kalkan, Sinan
It is now widely accepted that concepts and conceptualization are key elements towards achieving cognition on a humanoid robot. An important problem on this path is the grounded representation of individual concepts and the relationships between them. In this article, we propose a probabilistic method based on Markov Random Fields to model a concept web on a humanoid robot where individual concepts and the relations between them are captured. In this web, each individual concept is represented using a prototype-based conceptualization method that we proposed in our earlier work. Relations between concepts are linked to the cooccurrences of concepts in interactions. By conveying input from perception, action, and language, the concept web forms rich, structured, grounded information about objects, their affordances, words, etc. We demonstrate that, given an interaction, a word, or the perceptual information from an object, the corresponding concepts in the web are activated, much the same way as they are in humans. Moreover, we show that the robot can use these activations in its concept web for several tasks to disambiguate its understanding of the scene.


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In this thesis, we propose a formalization for a densely connected representation of concepts and their contexts on a humanoid robot platform. Although concepts have been studied implicitly and explicitly in numerous studies before,our study is unique in placing the relatedness of concepts to the center: We hypothesize that a concept is fully meaningful only when considered in relation to the other concepts. Thus, we propose a novel densely connected web of concepts, and show how utilizing the relatedness o...
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Recent studies in machine learning field proved that ideas which were once thought impractical are in fact tangible. Over the years, researchers have managed to develop learning systems which are able to interact with the environment and use experiences for adaptation to new conditions. Humanoid robots can now learn concepts such as nouns, adjectives and verbs, which is a big step for building human-like learners. Behind all these achievements, development of successful learning and classification technique...
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Citation Formats
H. Çelikkanat, G. Orhan, and S. Kalkan, “A Probabilistic Concept Web on a Humanoid Robot,” IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, pp. 92–106, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36205.