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    • Inamura, Tetsunari (3)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping ... Taniguchi, Akira; Hagiwara, Yoshinobu; Taniguchi, Tadahiro. - : arXiv, 2018 BASE Show details 2 Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping ... Taniguchi, Akira; Hagiwara, Yoshinobu; Taniguchi, Tadahiro. - : arXiv, 2017 BASE Show details 3 Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences ... Taniguchi, Akira; Taniguchi, Tadahiro; Inamura, Tetsunari. - : arXiv, 2016 Abstract: In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior knowledge of these words except for a primitive acoustic model. Further, we propose a method that allows a robot to effectively use the learned words and their meanings for self-localization tasks. The proposed method is nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the generative model for self-localization and the unsupervised word segmentation in uttered sentences via latent variables related to the spatial concept. We implemented the proposed method SpCoA on SIGVerse, which is a simulation environment, and TurtleBot2, which is a mobile robot in a real environment. Further, we conducted experiments for evaluating the performance of SpCoA. The experimental results showed that SpCoA enabled the robot to acquire the names of ... : This paper was accepted in the IEEE Transactions on Cognitive and Developmental Systems. (04-May-2016) ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Robotics cs.RO URL: https://arxiv.org/abs/1602.01208 https://dx.doi.org/10.48550/arxiv.1602.01208 BASE Hide details

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