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

Autonomous Robots, ISSN: 1573-7527, Vol: 44, Issue: 6, Page: 927-946
2020
  • 23
    Citations
  • 0
    Usage
  • 22
    Captures
  • 0
    Mentions
  • 34
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    23
    • Citation Indexes
      23
  • Captures
    22
  • Social Media
    34
    • Shares, Likes & Comments
      34
      • Facebook
        34

Article Description

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.

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