Improved and scalable online learning of spatial concepts and language models with mapping

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place cat...

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Bibliographic Details
Published in:Autonomous robots 2020-07, Vol.44 (6), p.927-946
Main Authors: Taniguchi, Akira, Hagiwara, Yoshinobu, Taniguchi, Tadahiro, Inamura, Tetsunari
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Computer Imaging
Control
Distance learning
Engineering
Machine learning
Mapping
Mechatronics
Pattern Recognition and Graphics
Probabilistic models
Robotics
Robotics and Automation
Training
Vision
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Summary:We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-020-09905-0