See like a Robot: Robot-Centric Pointmaps for VLA Models
Quick summary
arXiv:2607.11498v2 Announce Type: replace-cross Abstract: 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 R
Key takeaways
- arXiv:2607.11498v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“See like a Robot: Robot-Centric Pointmaps for VLA Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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