发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对长期物体搜索，提出回顾式开放词汇记忆ECROM，通过按机会推理处理删失观测，估计长期流行度并生成搜索先验，在HM3D基准上显著提升支持级AP和搜索SPL。
AI 中文摘要
长期物体搜索需要从对变化环境的重复但不均匀的观察中学习物体通常出现的位置。我们将回顾式开放词汇记忆表述为对删失观测的概率推断，其关键思想是按机会进行证据推理：一次检测或未检测应仅按机器人观测相应位置的机会比例影响信念。我们引入ECROM，它利用这一原则来估计仅在查询时指定的概念的长期流行度，并将所得信念直接转化为主动搜索先验。为评估该问题，我们在十个HM3D家庭中引入了一个受控的长期基准，该基准在重复遍历中独立变化物体放置和观测机会。ECROM在保留查询上的支持级别AP提高了4.5个百分点，在搜索SPL上比每个指标中最强的竞争记忆提高了4.2个百分点。基准、数据集和代码将开源。
英文摘要
Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate retrospective open-vocabulary memory as probabilistic inference from censored observations, where the key idea is to reason with evidence per opportunity: a detection or non-detection should influence belief only in proportion to the robot's opportunity to observe the corresponding location. We introduce ECROM, which uses this principle to estimate long-term prevalence for concepts specified only at query time and converts the resulting belief directly into an active-search prior. To evaluate this problem, we introduce a controlled long-term benchmark in ten HM3D homes that independently varies object placement and observation opportunity across repeated traversals. ECROM improves support-level AP on held-out queries by 4.5 points and search SPL by 4.2 points over the strongest competing memory in each metric. The benchmark, dataset, and code will be open-sourced. Project page: https://jiaming.im/ecrom/
Comments25 pages, 5 figures
