[Submitted on 17 Sep 2026]
Title:PIVOT: Physically Informed Vision-Language Off-Road Traversability for Field Robot Navigation
View a PDF of the paper titled PIVOT: Physically Informed Vision-Language Off-Road Traversability for Field Robot Navigation, by Aoran Jiao and 3 other authors
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Abstract:Terrain assessment is a critical capability for off-road mobile robots, enabling safe and reliable navigation through unstructured and geometrically complex environments. Conventional geometry-based terrain assessment is fast to compute but often overly conservative in unstructured environments. We present PIVOT: a Physically Informed Vision-Language Off-Road Traversability navigation system that augments conventional geometry-based planning with vision-language-model (VLM)-based semantic reasoning for field robots. To physically ground this assessment, we quantify how strongly the VLM's predicted traversal energy cost, robot vibration, and wheel slip correlate with real-world measurements and introduce a unified traversability score that weights each modality by its prediction-measurement correlation. For efficiency, we design a two-level navigation architecture that retains geometry-based planning as the nominal mode and invokes semantic replanning only when that mode fails to find a path. Across five repeated closed-loop trials on a mixed-terrain route totalling around $6.4$ km, the proposed system increases overall autonomy from $59.6\%$ to $97.0\%$, reduces human interventions from $11$ to $3$, and increases the mean distance between interventions (MDBI) from $69.2$ m to $412.9$ m compared with geometry-only navigation. These results demonstrate that physically grounded VLM-based terrain assessment can substantially extend autonomous navigation beyond the limitations of geometry alone, while preserving efficient geometric planning as the nominal mode.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.20983 [cs.RO]
(or arXiv:2609.20983v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.20983
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aoran Jiao [view email] [v1] Thu, 17 Sep 2026 18:34:18 UTC (16,437 KB)
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