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arXiv · 2312.05058

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

Daria de Tinguy·Toon van de Maele·Tim Verbelen·Bart Dhoedt
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Abstract

Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks. Inspired by human behaviour, this paper presents a scalable hierarchical active inference model for autonomous navigation, exploration, and goal-oriented behaviour. The model uses visual observation and motion perception to combine curiosity-driven exploration with goal-oriented behaviour. Motion is planned using different levels of reasoning, i.e., from context to place to motion. This allows for efficient navigation in new spaces and rapid progress toward a target. By incorporating these human navigational strategies and their hierarchical representation of the environment, this model proposes a new solution for autonomous navigation and exploration. The approach is validated through simulations in a mini-grid environment.

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Daria de Tinguy, Toon van de Maele, Tim Verbelen, Bart Dhoedt. 2024-09-13. Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment. https://doi.org/10.3390/e26010083

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