AAAI Publications, 2010 AAAI Fall Symposium Series

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Acquiring Vocabulary through Human Robot Interaction: A Learning Architecture for Grounding Words with Multiple Meanings
Aneesh Chauhan, Luís Seabra Lopes

Last modified: 2010-11-03


This paper presents a robust methodology for grounding vocabulary in robots. A social language grounding experiment is designed, where, a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. Any system for grounding vocabulary has to incorporate the properties of gradual evolution and lifelong learning. The learning model of the robot is adopted from an ongoing work on developing systems that conform to these properties. Significant modifications have been introduced to the adopted model, especially to handle words with multiple meanings. A novel classification strategy has been developed for improving the performance of each classifier for each learned category. A set of six new nearest-neighbor based classifiers have also been integrated into the agent architecture. A series of experiments were conducted to test the performance of the new model on vocabulary acquisition. The robot was shown to be robust at acquiring vocabulary and has the potential to learn a far greater number of words (with either single or multiple meanings).


language acquisition; open-ended category learning; instance-based learning; long-term learning; computer vision; multi-resolution analysis; incremental learing; symbol grounding; vocabulary acquisition; human robot interaction; embodied cognition

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