GrainGrasp：基于细粒度接触引导的灵巧抓取生成
GrainGrasp: Dexterous Grasp Generation with Fine-grained Contact Guidance
- Dalian University of Technology（大连理工大学）
- Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对灵巧手最优抓取策略生成难的问题，提出GrainGrasp方案，通过生成模型预测各指尖接触图、开发仅需点云的优化算法，实现精准类人抓取。
AI中文摘要：
灵巧机器人抓取的一个目标是让机器人能以与人类同等的灵活性与适应性操作物体。然而，为灵巧手生成最优抓取策略仍是一项具有挑战性的任务，尤其是在针对不同形状和尺寸的物体进行精细操作、精准调整所需抓取位姿时。本文提出了一种名为GrainGrasp的新型灵巧抓取生成方案，可为每根指尖提供细粒度的接触引导。具体而言，我们采用生成模型预测物体点云上每根指尖的独立接触图，有效捕捉手指-物体交互的具体特征。此外，我们开发了一种新的灵巧抓取优化算法，仅需点云作为输入，无需物体的完整网格信息。通过利用不同指尖的接触图，所提出的优化算法可生成精准、确定的类人物体抓取策略。实验结果证实了该方案的有效性。
英文摘要：
One goal of dexterous robotic grasping is to allow robots to handle objects with the same level of flexibility and adaptability as humans. However, it remains a challenging task to generate an optimal grasping strategy for dexterous hands, especially when it comes to delicate manipulation and accurate adjustment the desired grasping poses for objects of varying shapes and sizes. In this paper, we propose a novel dexterous grasp generation scheme called GrainGrasp that provides fine-grained contact guidance for each fingertip. In particular, we employ a generative model to predict separate contact maps for each fingertip on the object point cloud, effectively capturing the specifics of finger-object interactions. In addition, we develop a new dexterous grasping optimization algorithm that solely relies on the point cloud as input, eliminating the necessity for complete mesh information of the object. By leveraging the contact maps of different fingertips, the proposed optimization algorithm can generate precise and determinable strategies for human-like object grasping. Experimental results confirm the efficiency of the proposed scheme.
