KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization
Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. However, conventional behavior cloning (BC) methods often overfit to instance-specific geometry and appearance, limiting transfer to novel objects. We introduce KeyGen, a framework that learns canonicalized semantic 3D keypoints from point clouds and uses them as structured object-centric representations for policy learning. A visuomotor diffusion policy conditions on these keypoints together with object-centric geometry to predict full manipulation trajectories, enabling consistent geometric correspondence across object instances. To evaluat...
| Authors | Shuxin Cao, Liquan Wang, Masoud Moghani et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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