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One of the biggest challenges in intelligent robotics is to build robots that can learn to use language. To this end, we think that the practical long-term on-line concept/word learning algorithm for robots is a key issue to be addressed. In this paper, we develop an unsupervised on-line learning algorithm that uses Bayesian nonparametrics for categorizing multimodal sensory signals such as audio,...
Recent studies have shown that human beings unconsciously use signals that represent their thoughts and/or intentions when communicating with each other. These signals are known as “honest signals.” This study involves the use of a sociometer to capture multimodal data resulting from the interaction between humans. These data are then used to model the interaction using a multimodal hierarchical Dirichlet...
We propose a method for a robot to form various concepts. The robot uses its embodiment to obtain visual, auditory, and haptic information by grasping, shaking, and observing objects. At the same time, a user teaches the robot object features through speech. From these kinds of information, the robot can form object concepts. The information obtained by the robot is converted into Bag-of-Words representations,...
In this paper, we propose an online algorithm for multimodal categorization based on the autonomously acquired multimodal information and partial words given by human users. For multimodal concept formation, multimodal latent Dirichlet allocation (MLDA) using Gibbs sampling is extended to an online version. We introduce a particle filter, which significantly improve the performance of the online MLDA,...
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