GeoProp: Grounding Robot State in Vision for Generalist Manipulation
Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolated vector lacking explicit alignment with visual tokens. Without a direct correspondence between 3D kinematics and 2D feature maps, manipulation policies struggle to ground the robot's state within the scene, frequently underperforming even vision-only baselines. To address this, we introduce GeoProp, a lightweight, plug-and-play adapter that aligns proprioception with vision through explicit geometric grounding and spatial feature sampling. GeoProp projects the robot state onto the image plane to sample localized visual features, constructing a grounded state token. It then injec...
| Authors | Guoyang Zhao, Quanhao Qian, Gongjie Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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