AI 中文总结
研究携带行为注释地图的机器人的规划问题，通过将重新感知视为注意力决策，利用持久地图的记忆实现资源分配优化，在语言条件任务中表现出色，证明地图能指导机器人关注重点，而非空间导航。
AI 中文摘要
携带持久行为注释地图的机器人面临两个规划问题，但其记忆只能很好地回答其中一个。对于空间导航问题，基于视觉-语言-运动地图（VLMM）的行为感知规划器在28个AI2-THOR场景中可将规划时间目标成本降低约35%，但在闭环执行时实际收益几乎消失（约4%），按需视觉-语言模型（VLM）效果相当。对于资源分配问题，将重新感知视为注意力决策，持久地图的记忆能产生最佳调度，优于无记忆的VLM先验。记忆的好处集中在重要对象上，下游fetch任务中浪费的行程更少。当任务是语言条件时，VLMM能更好地定位和跟踪相关对象，优于相关加权近期基线和按需VLM。地图的价值在于告诉机器人关注什么，而非如何在房间里行走。
英文摘要
A robot carrying a persistent, behavior-annotated map faces two very different planning questions, and its memory answers only one of them 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 ~35% over 28 AI2-THOR scenes, but under closed-loop execution the benefit nearly vanishes (~4%) and an on-demand vision-language model (VLM) does as well. The resource-allocation question is different: under a limited perception budget, what should the robot attend to right now to keep its own 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 of the re-perception schedules we test (held-out), while the memoryless category prior is the weakest of them under the sqrt-law schedule -- though under a Whittle index it leads memory until heterogeneity is real. The gain grows with per-instance heterogeneity as a Cauchy-Schwarz bound predicts, tracking Var(sqrt(lambda)), the variance of root-volatility, and reallocates budget toward the important objects the schedule protects; against a real CLIP movability prior it is +21-26%, of which roughly half survives once that prior's saturated scale is calibrated (+7-13%). The map's full combination earns its keep when the task is language-conditioned: told what to keep track of, VLMM grounds the relevant objects (open-vocabulary) and tracks their change (memory), beating a relevance-weighted recency baseline (+2.9% over 26 queries, at full heterogeneity; the ordering reverses when instance rates track category norms) - and a category prior (+9.2%). 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.
