GraspGen-X: 跨形态6自由度扩散抓取
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
AI总结：
提出一种基于扩散模型的跨形态6自由度抓取方法，通过扫描体积启发式编码夹爪表示，在20亿抓取数据上训练，实现对新物体、场景和夹爪形态的零样本泛化。
摘要：
我们研究跨形态6自由度机器人抓取。与先前工作不同，我们要求模型不仅泛化到新物体/场景，还要泛化到新夹爪形态和物理抓取过程。我们的方法将基于扩散模型的生成式6自由度抓取模型扩展到对额外夹爪表示的条件化。我们提出一种用于编码夹爪的扫描体积启发式方法。我们使用程序化生成的夹爪和一个包含20亿抓取的大规模数据集训练跨形态模型。在仿真实验中，我们的模型在零样本泛化到新型真实世界夹爪和物体方面优于基线方法。我们的模型也可作为微调以适应新夹爪的良好初始化。在消融实验中，我们展示了扫描体积夹爪表示和程序化夹爪训练数据集的效率。最后，我们展示了在6自由度抓取中对真实世界新型夹爪的零样本泛化，在跨形态泛化方面超越了基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
