[Submitted on 23 Sep 2026]
Title:KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization
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Abstract: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 evaluate category-level generalization, we construct a photorealistic simulation benchmark with three manipulation tasks and a planning-driven data generation pipeline that produces expert trajectories across diverse object instances. Experiments show that KeyGen significantly outperforms prior methods on both seen and unseen objects under pose variation, scales effectively with additional demonstrations per object, maintains robustness to object rescaling, and achieves strong performance in both simulation and real-world manipulation.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.28818 [cs.RO]
(or arXiv:2609.28818v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.28818
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shuxin Cao [view email] [v1] Wed, 23 Sep 2026 21:53:10 UTC (6,685 KB)
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