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 the 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：强有力的证据表明，人类在探索环境时结合了拓扑地标和粗粒度路径整合。该方法依赖可识别的环境特征（拓扑地标）与距离和方向估算（粗粒度路径整合），以构建周围环境的认知地图。这种认知地图被认为具有层次结构，能够在解决复杂导航任务时实现高效规划。受人类行为启发，本文提出一种可扩展的层次主动推理模型，用于自主导航、探索及目标导向行为。该模型利用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。运动规划通过不同推理层级（即从情境到位置再到运动）实现，从而在新空间中高效导航并快速向目标推进。通过整合这些人类导航策略及其对环境的层次化表征，该模型为自主导航与探索提出了新方案。方法通过在微格栅环境中的仿真进行验证。