详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Junzhe Wu, Yue Hu, Zeyu Han, Po-Hsun Chang, Yinan Dong, Behrad Rabiei, Maani Ghaffari
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-23
摘要
arXiv:2607.19695v1 Announce Type: cross Abstract: Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored. We introduce NavVerse, a physics-enabled benchmark for indoor-to-outdoor embodied navigation. NavVerse contains 100 indoor scenes, 50 urban outdoor scenes, and 50 indoor-to-outdoor scenes, and 10,000 episodes spanning Object Navigation, Vision-and-Language Navigation, and Place Navigation tasks, where agents search for semantic points of interest such as restaurants or banks. Agents are evaluated through executable robot interfaces using task-success, path-efficiency, and safety metrics.
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