Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment
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.
Cite
@article{arxiv.2312.05058,
title = {Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment},
author = {Daria de Tinguy and Toon van de Maele and Tim Verbelen and Bart Dhoedt},
journal= {arXiv preprint arXiv:2312.05058},
year = {2024}
}
Comments
arXiv admin note: text overlap with arXiv:2309.09864