Problem
Research questions and friction points this paper is trying to address.
long-horizon navigation
evidence accumulation
visual history processing
NavProbe: Evidence-Grounded Reasoning with Active Memory Retrieval for Zero-Shot Navigation
🤖 AI Summary
为解决长距离导航中目标修订问题，NavProbe通过主动证据检索和动态子目标议程结合的方法，提高零样本导航性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
zero-shot navigation
active evidence retrieval
dynamic subgoal agenda
compact index
task executive
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