Learning adjectives and nouns from affordances on the iCub humanoid robot

Date

2012-09-14

Author

Yürüten, Onur
Uyanik, Kadir Firat
Çalişkan, Yiǧit
Bozcuoǧlu, Asil Kaan
Şahin, Erol
Kalkan, Sinan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

122
views

5
downloads

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-based studies that directly link the appearances of objects to nouns and adjectives, we first predict the affordances of an object through a set of Support Vector Machine classifiers which provided a functional view of the object. Then, we learned the mapping between these predicted affordance values and nouns and adjectives. We evaluated and compared a number of different approaches towards the learning of nouns and adjectives on a small set of novel objects. The results show that the proposed method provides better generalization than the appearance-based approaches towards learning adjectives whereas, for nouns, the reverse is the case. We conclude that affordances of objects can be more informative for (a subset of) adjectives describing objects in language. © 2012 Springer-Verlag.

Subject Keywords

adjectives, affordances, nouns

URI

https://hdl.handle.net/11511/96663

DOI

https://doi.org/10.1007/978-3-642-33093-3_33

Conference Name

12th International Conference on Simulation of Adaptive Behavior, SAB 2012

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

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...
Unsupervised Learning of Affordance Relations on a Humanoid Robot Akgun, Baris; Dag, Nilguen; Bilal, Tahir; Atil, Ilkay; Şahin, Erol (2009-09-16) In this paper, we study how the concepts learned by a robot can be linked to verbal concepts that humans use in language. Specifically, we develop a simple tapping behaviour on the iCub humanoid robot simulator and allow the robot to interact with a set of objects of different types and sizes to learn affordance relations in its environment. The robot records its perception, obtained from a range camera, as a feature vector, before and after applying tapping on an object. We compute effect features by subtr...
Building a web of concepts on a humanoid robot Orhan, Güner; Kalkan, Sinan; Department of Computer Engineering (2014) 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 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...
Implementing cognitive grammar on a cognitive architecture : a case study with act-r Stepanov, Evgueni A; Say, Bilge; Department of Cognitive Sciences (2004) Cognitive Grammar is a theory within the framework of Cognitive Linguistics that gives an account of human linguistic ability based entirely on general cognitive abilities. Because of the general complexity and open-endedness of the theory, there is not much computational work associated with it. This thesis proposes that ACT-R cognitive architecture can provide the basic primitives for the cognitive abilities required for a better implementation of Cognitive Grammar. Thus, a language model was developed on...

Citation Formats

O. Yürüten, K. F. Uyanik, Y. Çalişkan, A. K. Bozcuoǧlu, E. Şahin, and S. Kalkan, “Learning adjectives and nouns from affordances on the iCub humanoid robot,” Odense, Danimarka, 2012, vol. 7426 LNAI, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96663.