NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Quick summary
arXiv:2603.19229v2 Announce Type: replace-cross Abstract: There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agents navigate to a specified target object. However, existing work primarily evaluates model performance under nominal conditions, overlooking the potential corruptions that arise in real-world settings. To address this gap, we present NavTrust, a unified benchmark that systematically corrupts input modalities, including RGB, depth, and instru
Key takeaways
- arXiv:2603.19229v2 Announce Type: replace-cross Abstract: There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agents navigate to a specified target object.
- However, existing work primarily evaluates model performance under nominal conditions, overlooking the potential corruptions that arise in real-world settings.
- To address this gap, we present NavTrust, a unified benchmark that systematically corrupts input modalities, including RGB, depth, and instru
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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