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

Key points

• Generalist visual-navigation policies tell a robot which direction to go but not how far.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:12 AM

  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.

  Generalist visual-navigation policies tell a robot which direction to go but not how far.

Extractive summary: sentences quoted from the sources.