G-DexGrasp：通过部件感知先验检索与先验辅助生成实现可泛化的灵巧抓取合成
G-DexGrasp: Generalizable Dexterous Grasping Synthesis Via Part-Aware Prior Retrieval and Prior-Assisted Generation
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中文总结 AI 辅助
本文提出G-DexGrasp，通过检索细粒度接触部件和可供性分布作为先验，结合生成模型与正则化优化，实现面向未见物体类别和语言指令的可泛化灵巧抓取合成。
中文摘要 AI 辅助
近年来，灵巧抓取合成的最新进展表明，在为许多任务目的生成合理且可信的抓取方面已取得显著进步。然而，泛化到未见过的物体类别和多样化的任务指令仍然具有挑战性。在本文中，我们提出了G-DexGrasp，这是一种检索增强生成方法，能够为未见过的物体类别和基于语言的任务指令生成高质量的灵巧手配置。其关键在于检索可泛化的抓取先验，包括细粒度接触部件以及与相关抓取实例的可供性相关分布，用于后续的合成流程。具体而言，细粒度接触部件和可供性作为可泛化的引导，借助生成模型为未见过的物体推断合理的抓取配置，而相关抓取分布则作为正则化，在后续的细化优化过程中保证合成抓取的可信性。我们的对比实验验证了我们关键设计在泛化方面的有效性，并展示了相对于现有方法的显著性能。项目主页：https://g-dexgrasp.github.io/
英文摘要
Recent advances in dexterous grasping synthesis have demonstrated significant progress in producing reasonable and plausible grasps for many task purposes. But it remains challenging to generalize to unseen object categories and diverse task instructions. In this paper, we propose G-DexGrasp, a retrieval-augmented generation approach that can produce high-quality dexterous hand configurations for unseen object categories and language-based task instructions. The key is to retrieve generalizable grasping priors, including the fine-grained contact part and the affordance-related distribution of relevant grasping instances, for the following synthesis pipeline. Specifically, the fine-grained contact part and affordance act as generalizable guidance to infer reasonable grasping configurations for unseen objects with a generative model, while the relevant grasping distribution plays as regularization to guarantee the plausibility of synthesized grasps during the subsequent refinement optimization. Our comparison experiments validate the effectiveness of our key designs for generalization and demonstrate the remarkable performance against the existing approaches. Project page: https://g-dexgrasp.github.io/
发表机构
- Shenzhen University（深圳大学）
- Dalian University of Technology（大连理工大学）
- Shandong University（山东大学）
- Shenyang University of Technology（沈阳理工大学）
机构由 AI 辅助整理，请以论文原文为准。
