[Submitted on 22 Jul 2026]
Title:NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation
View a PDF of the paper titled NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation, by Junzhe Wu and 6 other authors
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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. Zero-shot experiments with RL, VLA, and modular baselines show that current agents remain far from solving cross-context navigation: end-to-end VLAs obtain the highest zero-shot success, while the modular method provides the strongest safety profile. PlaceNav further reveals a clear drop from outdoor to indoor-to-outdoor scenes, indicating that adaptation remains major bottleneck.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.19695 [cs.RO]
(or arXiv:2607.19695v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.19695
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Junzhe Wu [view email] [v1] Wed, 22 Jul 2026 02:53:46 UTC (16,824 KB)
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