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arXiv · 2610.16103

PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors

Gwanjun Shin·Hyeongseok Suh·Hongmo Jung·Jongwoo Lim
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Abstract

Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert their unitless trajectory into a metric path that a controller can track; existing scalar lifters apply uniform scaling that misses per-waypoint corrections, while in-policy approaches that inject monocular depth into the policy inherit the prediction errors of the depth model near obstacles and at close range. We present PathLift, a per-waypoint depth-conditioned adapter that lifts a pre-trained visual-navigation policy by consuming an explicit, sensor-grade depth source (RGB-D, stereo, or projected LiDAR) and fitting per-waypoint magnitude and lateral residuals to a canonicalized prior. PathLift consistently improves closed-loop navigation across diverse visual-navigation policies in simulation and on a real wheeled robot under both stereo and LiDAR depth. By turning the policy's unitless intent into a depth-feasible metric path in a single feed-forward pass, the adapter lifts real-world success without test-time optimization.

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Gwanjun Shin, Hyeongseok Suh, Hongmo Jung, Jongwoo Lim. 2026-10-12. PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors. https://arxiv.org/abs/2610.16103

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