感知机械臂的灵巧抓取生成：基于与机械臂无关的抓取模型
Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models
AI总结：
该研究提出一种感知机械臂的灵巧抓取生成框架，利用预训练的与机械臂无关的抓取模型，在6个场景的1万物体评估中，显著提升了高度约束场景下可行抓取的生成概率。
AI中文摘要：
在涉及机械臂环境碰撞规避、工作空间边界抓取及连续抓取的现实场景中，考虑机械臂相关约束的灵巧抓取生成至关重要。现有以手为中心的抓取模型主要关注漂浮手的位姿，无法满足此类场景需求。传统感知机械臂的方法要么依赖拒绝采样丢弃不可行样本，要么需在机械臂特定数据上重新训练，导致在不利条件下样本效率低，或在不同机器人和环境间泛化能力有限。为克服这些局限，本文提出一种感知机械臂的灵巧抓取生成框架，利用预训练的与机械臂无关的抓取模型，仅在推理时整合机械臂与环境信息。具体而言，我们将感知机械臂的约束抓取生成为手位姿与机械臂构型的联合优化，并推导机械臂相关约束的闭式梯度。假设手位姿分布由扩散模型表示，我们证明基于梯度的优化等价于引导扩散采样，可将接近可行的样本引导至可行区域。通过在6个场景中对1万个物体进行综合评估，我们证明所提框架在高度约束场景下生成可行抓取的概率显著更高，凸显其在现实应用中的优势。补充材料和附录可在此https URL获取。
英文摘要：
Dexterous grasp generation that considers arm-related constraints is crucial in real-world scenarios involving arm environment collision avoidance, workspace boundary grasps, and consecutive grasping. Existing hand-centric grasp models, which primarily focus on the floating hand's pose, are insufficient for such cases. Conventional arm-aware methods either rely on rejection sampling to discard infeasible samples or require retraining on arm-specific data, leading to low sample efficiency under adverse conditions or limited generalization across different robots and environments. To overcome these limitations, this letter presents an arm-aware dexterous grasp generation framework that leverages pretrained arm-agnostic grasp models while integrating arm and environmental information only at inference time. Specifically, we formulate arm-aware constrained grasp generation as a joint optimization of hand pose and arm configuration, and derive closed-form gradients for arm-related constraints. Assuming the hand pose distribution is represented by a diffusion model, we prove that gradient-based optimization is equivalent to guided diffusion sampling, steering near-feasible samples toward the feasible region. Through comprehensive evaluation involving 10k objects across 6 scenarios, we demonstrate that the proposed framework generates feasible grasps in highly constrained settings with significantly higher probability, highlighting its advantages in real-world applications. Supplementary materials and appendix are available at https://arm-aware-dexgrasp.github.io/.
