See like a Robot: Robot-Centric Pointmaps for VLA Models
Vision-language-action (VLA) models require 3D spatial reasoning, yet RGB observations encode robot-object geometry only implicitly. Lifting depth with camera intrinsics makes this geometry explicit as dense, image-aligned pointmaps, but their camera-frame coordinates depend on camera placement. We propose SeeR-VLA, which transforms pointmaps into a robot-centric frame with an end-effector origin and robot-base-aligned axes. An encoder initialized from pretrained RGB weights extracts pointmap features, which are added to corresponding RGB tokens without increasing the token count. Across 24 RoboCasa tasks and four real-world tasks, SeeR-VLA improves average success over RGB-only $π_{0.5}$...
| Authors | Byungkun Lee, Dongyoon Hwang, Dongjin Kim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 July 2026 |
| Last updated at source | 21 September 2026 |
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