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

arXiv: https://arxiv.org/abs/2606.00998 · HTML: https://mlsanity.com/paper/2606.00998/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2026.

## Built on by (most cited first, 3 of 3)

- [ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation](https://mlsanity.com/paper/2609.24563/) (2026)
- [CrossSafe: Towards Cross-Embodiment Latent Safety Filters](https://mlsanity.com/paper/2609.28984/) (2026)
- [Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion](https://mlsanity.com/paper/2609.33546/) (2026)

## Abstract (arXiv)

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.