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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Robot navigation as hierarchical active inference.

Ozan Çatal1, Tim Verbelen1, Toon Van de Maele1

  • 1Ghent University - imec, Belgium.

Neural Networks : the Official Journal of the International Neural Network Society
|May 22, 2021
PubMed
Summary
This summary is machine-generated.

This study unifies navigation, localization, and mapping under active inference. The hierarchical model explains mammalian navigation and enables robots to create maps and navigate effectively.

Keywords:
Active inferenceDeep learningRatSLAMRobot navigationSLAM

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Area of Science:

  • Neuroscience and Robotics

Background:

  • Localization and mapping are crucial for navigation in both biological systems and autonomous robots.
  • Existing models often address these components separately.

Purpose of the Study:

  • To present a unified framework for navigation, localization, and mapping using active inference.
  • To demonstrate the model's consistency with hippocampal function and its applicability in robotics.

Main Methods:

  • Treating navigation as inferring actions to minimize variational free energy within a hierarchical generative model.
  • Implementing the active inference model in silico on a real-world robot.

Main Results:

  • Perception, path integration, localization, and mapping emerge naturally from the active inference formulation.
  • The model successfully generates topologically consistent maps and infers correct navigation behavior in a robot.

Conclusions:

  • Active inference provides a unified theoretical basis for understanding navigation and mapping.
  • The proposed hierarchical model is biologically plausible and practically applicable for autonomous robot navigation.