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Spatial and Temporal Hierarchy for Autonomous Navigation Using Active Inference in Minigrid Environment.

Daria de Tinguy¹, Toon Van de Maele², Tim Verbelen²

¹IMEC, Ghent University, 9000 Gent, Belgium.

Entropy (Basel, Switzerland)

|January 22, 2024

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This study introduces a novel hierarchical active inference model for autonomous navigation, inspired by human exploration strategies. The model effectively combines curiosity-driven exploration with goal-oriented behavior for efficient navigation in new environments.

Area of Science:

Robotics
Cognitive Science
Artificial Intelligence

Background:

Humans navigate using topological landmarks and path integration, forming hierarchical cognitive maps for efficient planning.
Existing autonomous navigation models often lack the hierarchical structure and integrated exploration strategies seen in human behavior.

Purpose of the Study:

To present a scalable hierarchical active inference model for autonomous navigation, exploration, and goal-oriented behavior.
To integrate human-like navigational strategies, including curiosity-driven exploration and hierarchical environmental representation, into an AI model.

Main Methods:

Developed a hierarchical active inference model utilizing visual and motion perception.
Implemented a multi-level motion planning approach (context, place, motion) for navigation.

Keywords:

active inference autonomous navigation predictive coding spatial hierarchy temporal hierarchy

Related Experiment Videos

Validated the model through simulations in a mini-grid environment.

Main Results:

The model successfully combined curiosity-driven exploration with goal-oriented behavior.
Demonstrated efficient navigation in novel environments and rapid progress toward targets.
The hierarchical structure facilitated effective planning and navigation strategies.

Conclusions:

The proposed model offers a new solution for autonomous navigation and exploration by incorporating human-like hierarchical strategies.
This approach enhances the efficiency and adaptability of autonomous systems in complex environments.
Further research can explore real-world applications and more complex navigation tasks.