NDPP-Grasp：非可微物理合理性约束引导的任务导向灵巧抓取生成
NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
AI总结：
提出一种框架，通过将非可微物理合理性约束直接注入任务对齐的抓取扩散模型的去噪过程，实现物理合理性引导的灵巧抓取生成，同时保持任务对齐。
摘要：
任务导向的灵巧抓取生成旨在产生既物理合理又适用于特定操作任务的灵巧抓取姿态。现有的基于扩散的方法通常以解耦的方式处理这两个要求：它们首先训练一个用于任务对齐的抓取扩散模型，然后依赖生成后的细化来提高物理合理性。然而，这种事后修正策略仅在抓取已经生成后才应用物理合理性指导，使得生成轨迹本身不受物理约束引导，可能导致次优的抓取。为了解决这个问题，我们提出了一种新颖的框架，该框架以实用且有效的方式将物理合理性指导直接注入任务对齐的抓取扩散模型的去噪过程中，即使物理合理性约束是非可微的。这使得物理合理性能够在整个去噪过程中塑造抓取生成，同时保持任务对齐。大量实验证明了我们框架的有效性。
英文摘要：
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.
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。
