KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation

• URL: http://arxiv.org/abs/2606.09314v1
• Date: Mon, 08 Jun 2026 10:19:29 GMT
• Title: KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
• Abstract summary: We present KPGrasp, a flow-matching framework that learns dexterous grasp priors from large-scale data.<n>Experiments on 20 diverse objects demonstrate that the pipeline can be deployed in a real-world setup.
• Score: 23.028638559051597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating high-quality dexterous grasps remains challenging for learning-based methods, which often depend on carefully tuned contact losses or costly contact-based test-time refinement. We present KPGrasp, a flow-matching framework that learns dexterous grasp priors from large-scale data rather than relying on contact losses or contact-based test-time refinement. KPGrasp couples an all-Euclidean 3D hand-keypoint parameterization with a simple yet scalable Transformer flow model. The parameterization avoids the drawbacks of the conventional mixed SE(3) pose and joint-angle output space, expresses grasps in the same frame as the object point cloud, and thus enables native spatial reasoning; the Transformer flow model is trained with only the standard flow-matching loss and scales effectively with data, model capacity, and batch size. Experiments demonstrate state-of-the-art performance on two simulation benchmarks. On the Dexonomy benchmark, it reaches a 76.3% grasp success rate, improving over the strongest directly comparable baseline by 47.4% while reducing penetration depth to 2.4 mm. The same model also achieves the best average performance on the DexGrasp Anything benchmark without fine-tuning. For batched inference, KPGrasp requires only 0.032 s per grasp. Finally, real-world experiments on 20 diverse objects demonstrate that the pipeline can be deployed in a real-world setup.

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