标题： GraspGen：一种基于扩散的6-DOF抓取框架，具有生成器训练 显示英文标题

标题： GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training

摘要： 抓取是一项基本的机器人技能，尽管取得了显著的研究进展，基于学习的6-DOF抓取方法仍然不是即插即用的，并且在不同本体和真实环境设置中难以泛化。 我们建立在最近将物体中心抓取生成过程建模为迭代扩散过程的成功基础上。 我们提出的框架GraspGen，包括一个增强抓取生成的DiffusionTransformer架构，以及一个高效的判别器来对采样的抓取进行评分和过滤。 我们为判别器引入了一种新颖且高效的生成器训练方案。 为了将GraspGen扩展到物体和夹爪，我们发布了一个包含超过5300万次抓取的新模拟数据集。 我们证明了GraspGen在不同夹爪的单个物体模拟中优于先前方法，在FetchBench抓取基准上达到了最先进性能，并且在具有噪声视觉观测的真实机器人上表现良好。 显示英文摘要

摘要： Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp generation process as an iterative diffusion process. Our proposed framework, GraspGen, consists of a DiffusionTransformer architecture that enhances grasp generation, paired with an efficient discriminator to score and filter sampled grasps. We introduce a novel and performant on-generator training recipe for the discriminator. To scale GraspGen to both objects and grippers, we release a new simulated dataset consisting of over 53 million grasps. We demonstrate that GraspGen outperforms prior methods in simulations with singulated objects across different grippers, achieves state-of-the-art performance on the FetchBench grasping benchmark, and performs well on a real robot with noisy visual observations.