Authors:
Liesl Wigand
;
Monica Nicolescu
and
Mircea Nicolescu
Affiliation:
University of Nevada, United States
Keyword(s):
Machine Learning, Support Vector Machine, Isomap, Dimensionality Reduction, Classification.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Perception and Awareness
;
Robotics and Automation
Abstract:
The ability to learn new concepts is essential for any robot to be successful in real-world applications. This is due to the fact that it is impractical for a robot designer to pre-endow it with all the concepts that it would encounter during its operational lifetime. In this context, it becomes necessary that the robot is able to acquire new concepts, in a real-world context, from cues provided in natural, unconstrained interactions, similar to a human-teaching approach. However, existing approaches on concept learning from visual images and abstract concept learning address this problem in a manner that makes them unsuitable for learning in an embodied, real-world environment. This paper presents a developmental approach to concept learning. The proposed system learns abstract, generic features of objects and associates words from sentences referring to those objects with the features, thus providing a grounding for the meaning of the words. The method thus allows the system to lat
er identify such features in previously unseen images. The paper presents results obtained on data acquired with a Kinect camera and on synthetic images.
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