GraspGen-X：跨本体的基于扩散的六自由度抓取
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
把夹爪几何编码成扫掠体（swept-volume）条件注入扩散式 6-DOF 抓取生成模型，使同一模型可零样本迁移到未见过的夹爪形态；这套「以本体几何为条件」的思路可直接复用到视觉引导的抓取/操作策略中，也提供了 3.95 亿抓取规模的数据构造范式。
原文依据 We propose a swept-volume heuristic for encoding the gripper.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文研究跨本体的六自由度机器人抓取。与已有工作不同，我们要求模型不仅能够泛化到新物体与新场景，还要泛化到新的夹爪形态与物理抓取过程。我们的方法将基于扩散模型的生成式六自由度抓取模型扩展为以额外的夹爪表示为条件。我们提出一种扫掠体启发式方法来编码夹爪。我们使用程序化生成的夹爪以及一个包含 3.95 亿次抓取的大规模数据集来训练跨本体模型。在仿真实验中，相比基线方法，我们的模型对未见过的真实世界夹爪与物体具有最佳的零样本泛化能力。我们的模型也可作为良好的初始化，用于微调以适配新夹爪。在消融实验中，我们验证了扫掠体夹爪表示与程序化夹爪训练数据集的有效性。最后，我们展示了面向真实世界新夹爪的六自由度抓取零样本泛化能力，在跨本体泛化上超越基线。
英文原文摘要 1019 字符
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 395 Million 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.