Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long optimization times, while data-driven methods struggle with unseen objects. This paper formulates the problem as a rigid shape matching between gripper and object, which optimizes with Annealed Stein Iterative Closest Point (AS-ICP) and leverages GPU-based parallelization. By incorporating the gripper's tool center point and the object's center of mass into the cost function and using a signed distance field of the gripper for collision checking, our method achieves robust grasps with low computational time. Experiments with the Kinova KG3 gripper show an 87.3% success rate and 0.926 s computation time across various objects and settings, highlighting its potential for real-world applications.

翻译：抓取在机器人操作中至关重要，但由于物体与夹持器的多样性以及现实世界的复杂性，该任务极具挑战性。传统的解析方法通常优化时间较长，而数据驱动方法则难以应对未见过的物体。本文将抓取问题表述为夹持器与物体之间的刚性形状匹配，通过退火斯坦因迭代最近点（AS-ICP）算法进行优化，并利用基于GPU的并行化技术。通过将夹持器的工具中心点和物体的质心纳入成本函数，并使用夹持器的有符号距离场进行碰撞检测，我们的方法能够以较低的计算时间实现鲁棒的抓取。使用Kinova KG3夹持器进行的实验表明，在各种物体和场景下，该方法取得了87.3%的成功率和0.926秒的计算时间，凸显了其在现实应用中的潜力。