A Survey of Spatial Memory Representations for Efficient Robot Navigation

A robot carrying a persistent, behavior-annotated map faces two planning questions, and its memory answers only one well. The spatial-navigation question -- how to walk around a room -- we address first and report a negative: building on Vision--Language--Motion Maps (VLMM), a behavior-aware planner cost cuts a planning-time objective by $\sim$35% over 28 AI2-THOR scenes, but under closed-loop execution the real benefit nearly vanishes ($\sim$4%) and an on-demand vision--language model (VLM) does as well. The resource-allocation question differs: under a limited perception budget, what should the robot re-observe now to keep its map fresh? Framing re-perception as this attention decision, we show a persistent map's memory (change-history, or even just recency of last sighting) yields the best schedule (held-out), matching an oracle, while the memoryless VLM prior is poor. Because the schedule reallocates budget toward what matters, memory's benefit concentrates on the important objects ($\sim$1.6$\times$ the mean), and a downstream fetch task confirms fewer wasted trips; the gain grows with per-instance heterogeneity exactly as a Cauchy--Schwarz bound predicts -- it equals $\mathrm{Var}(\sqrtλ)$, the variance of root-volatility. With a real CLIP prior on rendered objects the advantage is $+21$--$26\%$. The map's distinctive value appears when the task is language-conditioned: told what to track, VLMM grounds the relevant objects (open-vocabulary) and tracks their change (memory), beating even a strong relevance-weighted recency baseline ($+2.5\%$) -- so its motion channel adds value beyond a last-seen timestamp -- and an on-demand VLM ($+8.9\%$); neither language nor dynamics alone suffices. The map earns its keep not by telling the robot how to walk around a room, but by telling it what to pay attention to.

Recent advances in large AI models (VLMs and LLMs) and joint use of the 3D dense maps, enable mobile robots to provide more powerful and interactive services grounded in rich spatial context. However, deploying both heavy AI models and dense maps on edge robots is challenging under strict memory budgets. When the memory budget is exceeded, required keyframes may not be loaded in time, which can degrade the stability of position estimation and interfering model performance. We proposes a semantic zone-based map management approach to stabilize dense-map utilization under memory constraints. We associate keyframes with semantic indoor regions (e.g., rooms and corridors) and keyframe management at the semantic zone level prioritizes spatially relevant map content while respecting memory constraints. This reduces keyframe loading and unloading frequency and memory usage. We evaluate the proposed approach in large-scale simulated indoor environments and on an NVIDIA Jetson Orin Nano under concurrent SLAM-VLM execution. With Qwen3.5:0.8b, the proposed method improves throughput by 3.3 tokens/s and reduces latency by 21.7% relative to a geometric map-management strategy. Furthermore, while the geometric strategy suffers from out-of-memory failures and stalled execution under memory pressure, the proposed method eliminates both issues, preserving localization stability and enabling robust VLM operation. These results demonstrate that the proposed approach enables efficient dense map utilization for memory constrained, AI-integrated mobile robots. Code is available at: https://github.com/huichangs/rtabmap/tree/segment

Spatial memory enables robots to reuse past observations for localization, navigation, and question answering. Designing such memory involves jointly deciding what to retain and how to retrieve it for diverse semantic, metric, and temporal needs. We investigate whether coding agents can automate this design process through iterative implementation and evaluation. Under a fixed development budget, a Designer Agent writes and revises code for memory construction and retrieval using feedback from a fixed Query Agent. The resulting implementations are frozen before building memories from held-out observation histories, without access to downstream questions, and evaluated across five complementary task tracks using the same Query Agent. Across three independent design searches, Codex / GPT-6 Astra averages 44.23 on the equally weighted five-track score, versus 29.32 for ReMEmbR, the strongest evaluated baseline supporting all five tracks, a 50.9% relative improvement. Qualitative analysis relates performance differences to evidence retention and retrieval, while identifying grounding and evidence-use failures. These results demonstrate the potential of coding agents to automate spatial memory design, while revealing task-dependent trade-offs in performance and computational cost. Project page: https://ba-fa-3344.github.io/.