{"ID":2921569,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T04:17:48.870468959Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00998","arxiv_id":"2606.00998","title":"GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping","abstract":"We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on the additional gripper's representation. We propose a swept-volume heuristic for encoding the gripper. We train our cross-embodiment model with procedural grippers and a large-scale dataset of 2 Billion grasps. In simulation experiments, our model has the best zero-shot generalization to novel real-world grippers and objects over baseline methods. Our model also serves as a good initialization for fine-tuning to adapt to novel grippers. In ablations, we demonstrate the efficiency of our sweep-volume gripper representation and our procedural gripper training dataset. Last, we show zero-shot generalization to real-world novel grippers for 6-DOF grasping, surpassing baselines in cross-embodiment generalization.","short_abstract":"We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on the additional gripp...","url_abs":"https://arxiv.org/abs/2606.00998","url_pdf":"https://arxiv.org/pdf/2606.00998v1","authors":"[\"Beining Han\",\"Yu-Wei Chao\",\"Erwin Coumans\",\"Clemens Eppner\",\"Balakumar Sundaralingam\",\"Jia Deng\",\"Stan Birchfield\",\"Adithyavairavan Murali\"]","published":"2026-05-31T04:28:18Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}