# AFFORD2ACT studies the capability ceiling of robot manipulation policies with sparse 2D keypoint input

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/tom8sr7r43l3i1jddfblsaozu
- Original: https://arxiv.org/abs/2510.01433

## Summary

AFFORD2ACT (Affordance-Guided Keypoints to Act) studies how much manipulation capability can be retained when only sparse 2D keypoints are used as input: an off-the-shelf language-conditioned affordance model locates task-relevant regions, selects up to 15 object keypoints plus 4 end-effector keypoints, compresses the policy input to 19 2D keypoints (38 coordinates), and then uses lightweight gating to decide which keypoints to focus on during execution.