CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Why can't José read?: the problem of learning semantic associations in a robot environment
HLT-NAACL-LWM '04 Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data - Volume 6
Neural Correlates of Concreteness in Semantic Categorization
Journal of Cognitive Neuroscience
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robotic vocabulary building using extension inference and implicit contrast
Artificial Intelligence
On the integration of grounding language and learning objects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Proceedings of the Workshop on Use of Context in Vision Processing
Learning Visual Object Categories for Robot Affordance Prediction
International Journal of Robotics Research
The iCub humanoid robot: an open platform for research in embodied cognition
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Learning Affordances for Categorizing Objects and Their Properties
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Simultaneously emerging Braitenberg codes and compositionality
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
IEEE Transactions on Autonomous Mental Development
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
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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 adjectives and nouns using features obtained from the appearance and affordances of an object, through cross-validated training as well as through testing on novel objects. The results indicate that shape-related adjectives are best learned using features related to affordances, whereas nouns are best learned using appearance features.Analysis of the feature relevancy shows that affordance features are more relevant for adjectives, and appearance features are more relevant for nouns. We show that adjective predictions can be used to solve the odd-one-out task on a number of examples. Finally, we link our results with studies from psychology, neuroscience and linguistics that point to the differences between the development and representation of adjectives and nouns in humans.