NavProbe：基于证据推理与主动记忆检索的零样本导航
NavProbe: Evidence-Grounded Reasoning with Active Memory Retrieval for Zero-Shot Navigation
- ShanghaiTech University（上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
NavProbe提出一种分层零样本导航智能体，通过动态子目标议程与主动证据检索，在R2R-CE、RxR-CE和HM3D-v2上取得领先性能，并支持真实机器人部署。
AI中文摘要：
长时程导航要求智能体随着证据的积累不断修正其中间目标。完整的视觉历史处理成本高昂，而紧凑的摘要可能遗漏重新考虑先前决策所需的细节。我们提出了NavProbe，一种分层零样本导航智能体，它将动态子目标议程与主动证据检索相结合。一个紧凑的索引将访问地点、转换和地标的摘要与其视觉和几何记录关联起来。当当前上下文不足时，任务执行器会检索有针对性的证据以生成、修正或解决子目标。可复用的结论用于更新索引，技能策略将修正后的任务状态转换为参数化的导航动作。NavProbe在R2R-CE上达到71.7%的成功率（SR）和55.8%的路径加权长度（SPL），在RxR-CE上达到55.3%的SR和38.6%的SPL，优于强零样本基线。它还在HM3D-v2 ObjectNav上达到79.3%的SR，并通过定性真实机器人演示展示了物理部署。
英文摘要：
Long-horizon navigation requires an agent to revise its intermediate objectives as evidence accumulates. Full visual histories are costly to process, while compact summaries may omit details needed to reconsider earlier decisions. We introduce NavProbe, a hierarchical zero-shot navigation agent that couples a dynamic subgoal agenda with active evidence retrieval. A compact index links summaries of visited places, transitions, and landmarks to their visual and geometric records. When the current context is insufficient, a task executive retrieves targeted evidence to generate, revise, or resolve subgoals. Reusable conclusions are used to update the index, and a skill policy converts the revised task state into parameterized navigation actions. NavProbe achieves 71.7% SR and 55.8% SPL on R2R-CE and 55.3% SR and 38.6% SPL on RxR-CE, outperforming strong zero-shot baselines. It also achieves 79.3% SR on HM3D-v2 ObjectNav, with qualitative real-robot demonstrations illustrating physical deployment. Code is available at https://github.com/liujy25/NavProbe.
