自信记忆：具有概率保证的时空记忆不确定性量化
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
AI总结：
针对机器人长期操作中VLM描述噪声大、视角不一致的问题，提出目标级语义不确定性评分，并集成到UQ-DAAAM系统中，通过主动选择高质量视图和融合多视角描述来降低不确定性，同时提供概率保证。
摘要：
长期机器人操作需要时空记忆来记录环境状态并在下游推理中回忆。场景图和检索增强系统将VLM描述锚定到持久的3D实体，并带有丰富的语义描述。然而，VLM描述存在噪声且视角不一致，现有系统将其视为神谕，没有机制检测不可靠的存储描述。我们引入了多视角VLM记忆的目标级语义不确定性：一种衡量目标中心跨视角语义描述分散度并识别语义未解决目标的分数。然后，我们将不确定性分数集成到一个高级空间语义记忆系统中，称为UQ-DAAAM。UQ-DAAAM利用该分数，在固定查询预算下通过选择高质量视图并将多视角描述融合为单一目标描述，主动优化不确定目标。我们还推导了概率保证，表明更高质量的候选视图（根据我们的方法选择）更有可能降低不确定性。实验表明，不确定性量化可以使具身4D记忆系统更可靠、更有效。特别是在OC-NaVQA基准上，UQ-DAAAM相比基线实现了显著更大的不确定性降低和更好的时空问答性能。
英文摘要：
Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems ground VLM descriptions to persistent 3D entities with rich semantic descriptions. However, VLM captions are noisy and viewpoint-inconsistent, and existing systems treat them as an oracle with no mechanism to detect unreliable stored descriptions. We introduce object-level semantic uncertainty for multi-view VLM memory: a score that measures object-centric cross-view semantic scatter of captions and identifies semantically unresolved objects. Then, we include our uncertainty scores in an advanced spatial-semantic memory system, that we dub UQ-DAAAM. UQ-DAAAM uses this score to actively refine uncertain objects under a fixed query budget by selecting high-quality views and fusing the resulting multi-view captions into a single object description. We also derive probabilistic guarantees showing that higher-quality candidate views (as selected by our approach) are more likely to reduce uncertainty. Our experiments show that uncertainty quantification can make embodied 4D memory systems more reliable and more effective. In particular, on the OC-NaVQA benchmark, UQ-DAAAM achieves substantially larger uncertainty reduction and better spatio-temporal question answering performance than baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
