GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping

• URL: http://arxiv.org/abs/2606.00998v1
• Date: Sun, 31 May 2026 04:28:18 GMT
• Title: GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
• Abstract summary: We train our cross-embodiment model with procedural grippers and a large-scale dataset of 2 Billion grasps.<n>Our model has the best zero-shot generalization to novel real-world grippers and objects over baseline methods.<n>Last, we show zero-shot generalization to real-world novel grippers for 6-DOF grasping, surpassing baselines in cross-embodiment generalization.
• Score: 28.903101018539143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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