Improved and scalable online learning of spatial concepts and language models with mapping
Improved and scalable online learning of spatial concepts and language models with mapping
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DOI：
10.1007/s10514-020-09905-0
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发表时间：
2020-02-08
影响因子：
3.5
通讯作者：
Inamura, Tetsunari
中科院分区：
文献类型：
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作者：
Taniguchi, Akira;Hagiwara, Yoshinobu;Inamura, Tetsunari
我们提出了一种新的在线学习算法，称为SpCoSLAM 2.0，空间概念和词汇获取具有高精度和可扩展性。在此之前，我们提出了SpCoSLAM作为一个在线学习算法的基础上无监督贝叶斯概率模型，集成了多模态的地方分类，词汇采集，和SLAM。然而，我们的原始算法由于学习的早期阶段的影响而具有有限的估计精度，并且随着训练数据的增加而增加计算复杂度。因此，我们引入了固定滞后恢复等技术来减少计算时间，同时保持比原始算法更高的精度。结果表明，在估计精度方面，所提出的算法超过了原算法，并与批学习相当。此外，所提出的算法的计算时间不依赖于训练数据的量，并成为常数的可扩展算法的每一步。我们的方法将有助于实现人类和机器人之间的长期空间语言交互。
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.