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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
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Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.

Yoshinobu Hagiwara1, Masakazu Inoue1, Hiroyoshi Kobayashi1

  • 1Emergent Systems Laboratory, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan.

Frontiers in Neurorobotics
|March 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a hierarchical spatial concept formation method for robots, enabling better human-robot communication. The approach uses multimodal data to allow robots to understand and predict spatial information like humans do.

Keywords:
hierarchyhuman support robothuman-robot interactionmultimodal categorizationspatial conceptunsupervised learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Human support robots require sophisticated spatial understanding for effective user interaction.
  • Current robot systems often lack the nuanced, hierarchical spatial reasoning capabilities of humans.
  • Linguistic descriptions of location (e.g., "near the table") necessitate abstract spatial concept formation.

Purpose of the Study:

  • To develop a hierarchical spatial concept formation method for robots using a Bayesian generative model.
  • To enable robots to categorize multimodal information (vision, position, language) into a hierarchical structure.
  • To improve human-robot communication by allowing robots to understand and generate spatial concepts similarly to humans.

Main Methods:

  • Proposed a hierarchical multimodal latent Dirichlet allocation (hMLDA) model.
  • Integrated object recognition (CNN), self-position estimation (MCL), and linguistic location data.
  • Utilized a real-world robot platform for experimentation and validation.

Main Results:

  • The proposed method successfully formed hierarchical spatial concepts.
  • Robots demonstrated improved prediction of unobserved location names and position categories compared to baseline methods.
  • The system achieved human-like accuracy in predicting spatial information.

Conclusions:

  • The hierarchical spatial concept formation method enhances robot's ability to interpret and utilize spatial information.
  • This advancement facilitates smoother communication and interaction between humans and robots.
  • The method has practical applications, such as enabling robots to navigate based on spoken instructions.