让抓取生成过程始终符合物理合理性，提升操作适配性。
NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
- 在去噪过程中直接融入物理合理性约束，全程引导抓取生成
- 无需可微分约束，仍能有效提升抓取的物理可行性
- 适合需要高精度抓取的机器人操作任务
任务导向的灵巧抓取生成旨在生成既符合物理现实又适配特定操作任务的灵巧抓取姿态。现有基于扩散模型的方法通常分步处理：先训练抓取扩散模型以对齐任务目标，再通过后处理优化物理合理性。然而这种事后修正策略仅在生成后施加物理约束，无法引导生成轨迹本身，可能导致次优抓取。为此，我们提出一种新框架，将物理合理性约束以实用有效的方式直接注入任务对齐的抓取扩散模型的去噪过程，即使约束不可微也依然可行。该方法使物理合理性贯穿整个去噪过程，同时保持任务对齐。大量实验验证了框架的有效性。
原文摘要 · Abstract (English)
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.
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