Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping

TL;DR

This paper introduces SpCoSLAM 2.0, an improved online learning algorithm for spatial concepts and language models that offers higher accuracy and scalability, enabling long-term human-robot spatial language interactions.

Contribution

The paper presents SpCoSLAM 2.0, a novel scalable online learning algorithm that enhances accuracy and reduces computational complexity compared to previous methods.

Findings

Estimates spatial concepts and language models with higher accuracy.

Achieves constant computation time regardless of training data size.

Comparable performance to batch learning methods.

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.