通过检索通用抓取先验，让机械手学会抓未知物体并理解语言指令。
G-DexGrasp: Generalizable Dexterous Grasping Synthesis Via Part-Aware Prior Retrieval and Prior-Assisted Generation
- 检索物体接触部位与抓取功能分布作为通用引导
- 在未见过的物体和指令下生成合理且可信的抓取姿态
- 适合机器人抓取、具身智能等需要泛化能力的研究者
近期的灵巧抓取合成研究在生成多种任务所需的合理抓取方面取得了显著进展，但仍难以泛化到未见物体类别和多样化的任务指令。本文提出 G-DexGrasp，一种基于检索增强生成的方法，可为未见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。核心思想是检索可泛化的抓取先验，包括细粒度接触部位以及相关抓取实例的功能性分布，用于后续合成流程。具体而言，细粒度接触部位与功能性作为通用指导，帮助生成模型推断未见物体的合理抓取配置；而相关抓取分布则在后续优化阶段起到正则化作用，确保生成抓取的合理性。对比实验验证了所提设计在泛化性能上的有效性，并展现出优于现有方法的显著表现。
原文摘要 · Abstract (English)
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/
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