抓取如梦：从生成的人类示范中模仿功能抓取
Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
AI总结：
本文提出GraspDreamer方法，利用视觉生成模型合成的人类示范实现零样本功能抓取，通过隐含的通用先验知识和动作优化，提升数据效率和泛化能力。
摘要：
构建能够执行日常开放世界中功能抓取的通用机器人仍面临重大挑战，由于物体和任务的多样性巨大。现有方法要么受限于狭窄的对象/任务集，要么依赖 prohibitively 大规模的数据收集来捕捉现实世界的变异性。在本文中，我们提出了一种替代方法，GraspDreamer，一种利用通过视觉生成模型（VGMs）合成的人类示范来实现零样本功能抓取的方法，无需劳动密集型的数据收集。关键思想是VGMs在大规模互联网人类数据上预训练，隐含编码了人类与物理世界互动的通用先验知识，可以与特定具身的动作优化结合，以最小的努力实现功能抓取。在公开基准上对不同机械手的大量实验展示了GraspDreamer相比先前方法的优越的数据效率和泛化性能。现实世界评估进一步验证了其在真实机器人上的有效性。此外，我们展示了GraspDreamer可以（1）自然扩展到下游操作任务，（2）可以生成数据以支持视觉-运动策略学习。
英文摘要：
Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and tasks. Existing methods are either constrained to narrow object/task sets or rely on prohibitively large-scale data collection to capture real-world variability. In this work, we present an alternative approach, GraspDreamer, a method that leverages human demonstrations synthesized by visual generative models (VGMs) (e.g., video generation models) to enable zero-shot functional grasping without labor-intensive data collection. The key idea is that VGMs pre-trained on internet-scale human data implicitly encode generalized priors about how humans interact with the physical world, which can be combined with embodiment-specific action optimization to enable functional grasping with minimal effort. Extensive experiments on the public benchmarks with different robot hands demonstrate the superior data efficiency and generalization performance of GraspDreamer compared to previous methods. Real-world evaluations further validate the effectiveness on real robots. Additionally, we showcase that GraspDreamer can (1) be naturally extended to downstream manipulation tasks, and (2) can generate data to support visuomotor policy learning.
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。
