Building a web of concepts on a humanoid robot

Orhan, Güner
In this thesis, an effective approach for predicting nouns, adjectives and verbs is introduced for more effective communication between a humanoid robot and a human actor. There are three important challenges addressed by our approach: The first one is the accurate prediction of words in language. Most of the existing robotics studies predict words in language using perceptual information only. However, due to noise and ambiguity in low-level sensory information, prediction using perceptual information is often incorrect. The second challenge is the meaning of the words. The existing studies mostly use discriminative methods to predict words, yet the underlying semantics of what, e.g., a certain noun represents, is not adequately addressed in the literature. The third challenge is representation of the relations between the different words in language. It is known that humans activate in their brains not only the meaning of the word when that word is uttered but also the related words and their meaning. However, this challenge has not been addressed in the robotics literature. In this thesis, the words in language are first conceptualized and gradually, a web of concepts is built from the interactions of the robot. The web is built using the co-occurrence information of words, modeled as a Markov Random Field and trained using Loopy Belief Propagation, a widely-used method for such tasks. The thesis shows on iCub, a humanoid robot, that such a web of concepts addresses to a certain extent all the challenges discussed above: the web improves prediction of word categories; it represents the meaning of words in concepts, and it represents the relations between the words and their meaning. As such, this thesis makes a first important step towards grounded representation of a semantic network on a humanoid robot, which can be used for several high-level cognitive tasks, such as contextual reasoning, planning, language understanding, etc.


A Grounded and contextualized web of concepts on a humanoid robot
Çelikkanat, Hande; Kalkan, Sinan; Şahin, Erol; Department of Computer Engineering (2015)
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...
Formation of adjective, noun and verb concepts through affordances
Yürüten, Onur; Kalkan, Sinan; Şahin, Erol; Department of Computer Engineering (2012)
In this thesis, we study the development of linguistic concepts (corresponding to a subset of nouns, verbs and adjectives) on a humanoid robot. To accomplish this goal, we use affordances, a notion first proposed by J.J. Gibson to describe the action possibilities offered to an agent by the environment. Using the affordances formalization framework of Sahin et al., we have implemented a learning system on a humanoid robot and obtained the required data from the sensorimotor experiences of the robot. The sys...
Learning adjectives and nouns from affordances on the iCub humanoid robot
Yürüten, Onur; Uyanik, Kadir Firat; Çalişkan, Yiǧit; Bozcuoǧlu, Asil Kaan; Şahin, Erol; Kalkan, Sinan (2012-09-14)
This article studies how a robot can learn nouns and adjectives in language. Towards this end, we extended a framework that enabled robots to learn affordances from its sensorimotor interactions, to learn nouns and adjectives using labeling from humans. Specifically, an iCub humanoid robot interacted with a set of objects (each labeled with a set of adjectives and a noun) and learned to predict the effects (as labeled with a set of verbs) it can generate on them with its behaviors. Different from appearance...
Simple and complex behavior learning using behavior hidden Markov Model and CobART
Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Department of Computer Engineering (2013)
In this thesis, behavior learning and generation models are proposed for simple and complex behaviors of robots using unsupervised learning methods. Simple behaviors are modeled by simple-behavior learning model (SBLM) and complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models have common phases named behavior categorization, behavior modeling, and behavior generation. Sensory data are categorized using correlation based...
The learning of adjectives and nouns from affordance and appearance features
Yürüten, Onur; Şahin, Erol; Kalkan, Sinan (SAGE Publications, 2013-8-22)
We study how a robot can link concepts represented by adjectives and nouns in language with its own sensorimotor interactions. Specifically, an iCub humanoid robot interacts with a group of objects using a repertoire of manipulation behaviors. The objects are labeled using a set of adjectives and nouns. The effects induced on the objects are labeled as affordances, and classifiers are learned to predict the affordances from the appearance of an object. We evaluate three different models for learning adjecti...
Citation Formats
G. Orhan, “Building a web of concepts on a humanoid robot,” M.S. - Master of Science, Middle East Technical University, 2014.