Abstract
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.
Highlights
Robots operating in various human environments must adaptively and sequentially acquire new categories for places and unknown words related to various places as well as the map of the environment (Kostavelis and Gasteratos 2015)
We evaluated the different algorithms according to the following metrics: the Adjusted Rand Index (ARI) (Hubert and Arabie 1985) of the classification results of spatial concepts C1:N and position distribution i1:N ; the Estimation Accuracy Rate (EAR) of the estimated total numbers of spatial concepts L and position distributions K ; and the Phoneme Accuracy Rate (PAR) of uttered sentences and words related to places
In (A), incorrect clustering results were obtained during the final step because the original SpCoSLAM algorithm cannot correct past erroneous estimations
Summary
Robots operating in various human environments must adaptively and sequentially acquire new categories for places and unknown words related to various places as well as the map of the environment (Kostavelis and Gasteratos 2015). The robot obtains the present estimated position, the scene image, and the speech signal at that time, and acquires spatial knowledge regarding the environment, such as the relationship between words and places. We proposed SpCoSLAM as an integrated model of nonparametric Bayesian multimodal categorization, a Bayesian filter-based SLAM, speech recognition, and word segmentation, from the standpoint of unsupervised machine learning. This algorithm (Taniguchi et al 2017) had inferior accuracy in terms of categorization and word segmentation compared to batch learning, owing to a situation whereby sufficient statistical information could not be used at the early stages of learning.
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