CanonNav: Disentangling Navigation Behavior from Camera Geometry in Cross-Platform Visual Navigation

• URL: http://arxiv.org/abs/2608.30242v1
• Date: Mon, 31 Aug 2026 04:52:54 GMT
• Title: CanonNav: Disentangling Navigation Behavior from Camera Geometry in Cross-Platform Visual Navigation
• Abstract summary: CanonNav is a visual navigation framework that disentangles navigation behavior from camera geometry.<n>It incorporates complementary planning supervision into learning from cross-platform demonstrations.<n>It consistently outperforms RGB-based baselines and even surpasses RGB-D-based methods in challenging scenarios.
• Score: 14.558656853501846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While visual navigation has advanced through imitation learning from cross-platform demonstrations, fully leveraging such data remains challenging. First, directly learning from image-trajectory pairs entangles navigation behavior with platform-dependent camera geometry. This hinders consistent learning by forcing the policy to implicitly infer camera geometry from visual observations, an inherently ill-posed problem. Second, imitation learning from demonstrated trajectories captures the expert's chosen motion but leaves the intermediate decisions underlying that motion implicit. To address these issues, we propose CanonNav, a visual navigation framework that disentangles navigation behavior from camera geometry and incorporates complementary planning supervision into learning from cross-platform demonstrations. CanonNav introduces camera geometry canonicalization, which transforms visual observations and trajectories into a camera-consistent representation space. Building on this representation, we derive safety and local-progress supervision using pseudo-labels from an offline traversability estimator. Safety supervision penalizes unsafe trajectories, while local-progress supervision guides where the robot should advance. Experiments across diverse camera configurations and environments show that, despite using only RGB at inference, CanonNav consistently outperforms RGB-based baselines and even surpasses RGB-D-based methods in challenging scenarios.

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