用形状匹配方法实现快速稳健抓取，适合真实场景机器人操作。
Grasping by parallel shape matching
- 将抓取建模为夹爪与物体的刚性形状匹配，结合AS-ICP优化与GPU并行计算。
- 在多种物体上达到87.3%抓取成功率，平均计算时间仅0.926秒。
- 适用于需快速响应的真实机器人抓取任务，尤其适合复杂多变环境。
抓取是机器人操作中的关键任务，但受限于物体与夹爪多样性及现实复杂性，仍具挑战。传统解析方法优化耗时长，数据驱动方法对未见物体泛化能力差。本文将抓取问题建模为夹爪与物体间的刚性形状匹配，采用退火斯坦因迭代最近点（AS-ICP）进行优化，并利用GPU并行加速。通过在代价函数中引入夹爪工具中心点和物体质心信息，并使用夹爪的符号距离场进行碰撞检测，实现了低计算开销下的鲁棒抓取。在Kinova KG3夹爪上的实验表明，该方法在多种物体与设置下达到87.3%的成功率，平均计算时间仅为0.926秒，展现出在实际应用中的巨大潜力。
原文摘要 · Abstract (English)
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.
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