An interactive framework for learning user-object associations through human-robot interaction

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Conference Proceedings (fully refereed)
M. Papakostas, K. Tsiakas, N. Parde, V. Karkaletsis & F. Makedon
A great deal of recent research has focused on social and assistive robots that can achieve a more natural and realistic interaction between the agent and its environment. Following this direction, this paper aims to establish a computational framework that can associate objects with their uses and their basic characteristics in an automated manner. The goal is to continually enrich the robot's knowledge regarding objects that are important to the user, through verbal interaction. We address the problem of learning correlations between object properties and human needs by associating visual with verbal information. Although the visual information can be acquired directly by the robot, the verbal information is acquired via interaction with a human user. Users provide descriptions of the objects for which the robot has captured visual information, and these two sources of information are combined automatically. We present a general model for learning these associations using Gaussian Mixture Models. Since learning is based on a probabilistic model, the approach handles uncertainty, redundancy, and irrelevant information. We illustrate the capabilities of our approach by presenting the results of an initial experiment run in a laboratory environment, and we describe the set of modules that support the proposed framework.
Software and Knowledge Engineering Laboratory (SKEL)
Conference Short Name: 
Conference Full Name: 
8th ACM International Conference on PErvasive Technologies Related to Assistive Environments
Conference City: 
Conference Date(s): 
Wed, 01/07/2015 - Fri, 03/07/2015
Conference Level: 

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