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StereoPatch: Patch-Aligned RGB-Depth Fusion for Spatial Perception in Robot Manipulation

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Recent advances in robot imitation learning have produced visuomotor policies that predict actions directly from visual observations. Yet visually similar scenes can require different actions as target position, object height, or contact geometry changes. Pretrained RGB features may map these geometrically distinct states to similar policy inputs, while simply adding depth requires the policy to learn RGB-depth correspondence from the same limited demonstrations used to learn control. We introduce StereoPatch, a patch-aligned RGB-depth representation that binds registered metric geometry direc

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