Task-oriented dexterous grasp generation aims to produce dexterous grasp poses that are both physically plausible and functionally suitable for specified manipulation tasks. Existing diffusion-based methods often address these two requirements in a decoupled manner: they first train a grasp diffusion model for task alignment and then rely on post-generation refinement to improve physical plausibility. However, this after-the-fact correction strategy applies physical plausibility guidance only once the grasp has already been generated, leaving the generation trajectory itself unguided by physical constraints and potentially leading to suboptimal grasps. To address this problem, we propose a novel framework that directly injects physical plausibility guidance into the denoising process of a task-aligned grasp diffusion model in a practical and effective manner, even when physical plausibility constraints are non-differentiable. This allows physical plausibility to shape grasp generation throughout denoising while preserving task alignment. Extensive experiments demonstrate the efficacy of our framework.

翻译：任务导向的灵巧抓取生成旨在产出既符合物理合理性、又适用于特定操作任务的灵巧抓取姿态。现有基于扩散的方法通常以解耦方式处理这两项要求：首先训练一个用于任务对齐的抓取扩散模型，随后依赖生成后精炼来提升物理合理性。然而，这种事后修正策略仅在抓取已生成后才施加物理合理性引导，导致生成轨迹本身缺乏物理约束的指导，可能产生次优的抓取结果。针对这一问题，我们提出了一种新颖框架，即使物理合理性约束不可微，也能以实用且有效的方式将物理合理性引导直接注入任务对齐的抓取扩散模型的去噪过程中。这使得物理合理性能够在去噪全程塑造抓取生成过程，同时保持任务对齐。大量实验证明了我们框架的有效性。