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.

翻译：有力证据表明，人类在探索环境时会综合运用拓扑地标与粗粒度路径整合策略。该方法利用可识别的环境特征（拓扑地标），结合距离与方向估计（粗粒度路径整合），构建环境的认知地图。该认知地图被认为具有层次化结构，可支持复杂导航任务中的高效规划。受人类行为启发，本文提出一种可扩展的层次化主动推理模型，用于自主导航、探索及目标导向行为。该模型通过视觉观测与运动感知，将好奇心驱动的探索与目标导向行为相结合。运动规划采用不同推理层级（即从情境到位置再到运动），从而在陌生空间中实现高效导航并快速接近目标。通过整合人类导航策略及其对环境的分层表征，本模型为自主导航与探索提供了新方案。在微型网格环境中的仿真实验验证了该方法的有效性。