Animals and robots navigate through environments by building and refining maps of space. These maps enable functions including navigation back to home, planning, search and foraging. Here, we use observations from neuroscience, specifically the observed fragmentation of grid cell map in compartmentalized spaces, to propose and apply the concept of Fragmentation-and-Recall (FARMap) in the mapping of large spaces. Agents solve the mapping problem by building local maps via a surprisal-based clustering of space, which they use to set subgoals for spatial exploration. Agents build and use a local map to predict their observations; high surprisal leads to a "fragmentation event" that truncates the local map. At these events, the recent local map is placed into long-term memory (LTM) and a different local map is initialized. If observations at a fracture point match observations in one of the stored local maps, that map is recalled (and thus reused) from LTM. The fragmentation points induce a natural online clustering of the larger space, forming a set of intrinsic potential subgoals that are stored in LTM as a topological graph. Agents choose their next subgoal from the set of near and far potential subgoals from within the current local map or LTM, respectively. Thus, local maps guide exploration locally, while LTM promotes global exploration. We demonstrate that FARMap replicates the fragmentation points observed in animal studies. We evaluate FARMap on complex procedurally-generated spatial environments and realistic simulations to demonstrate that this mapping strategy much more rapidly covers the environment (number of agent steps and wall clock time) and is more efficient in active memory usage, without loss of performance. https://jd730.github.io/projects/FARMap/

翻译：动物与机器人通过构建并优化空间地图实现环境导航。这些地图支持包括返巢导航、路径规划、搜索与觅食在内的多种功能。本文借鉴神经科学观测成果——特别是网格细胞地图在分隔空间中的碎片化现象——提出并应用“碎片化-召回”（FARMap）概念以实现大尺度空间建图。智能体通过基于意外度的空间聚类构建局部地图来解决建图问题，并以此设定空间探索的子目标。智能体构建并使用局部地图预测观测结果；高意外度将触发“碎片化事件”，从而截断当前局部地图。在此类事件中，近期局部地图将被存入长期记忆（LTM）并初始化新局部地图。若碎片化节点的观测值与已存储的某个局部地图匹配，则该地图将从LTM中召回（即复用）。这些碎片化节点自然形成大尺度空间的在线聚类，构成一组存储在LTM拓扑图中的内在潜在子目标。智能体分别从当前局部地图（近程）或LTM（远程）的潜在子目标集合中选择下一子目标。因此，局部地图指导局部探索，而LTM促进全局探索。我们证明FARMap能够复现动物研究中观察到的碎片化节点。通过在复杂程序生成空间环境与真实仿真场景中的评估，表明该建图策略能以更少的智能体步数与实耗时实现更快速的环境覆盖，且在保持性能的同时显著提升活动内存使用效率。https://jd730.github.io/projects/FARMap/