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arXiv · 2412.08346

Grasping by parallel shape matching

Wenzheng Zhang·Fahira Afzal Maken·Tin Lai·Fabio Ramos
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Abstract

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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Wenzheng Zhang, Fahira Afzal Maken, Tin Lai, Fabio Ramos. 2024-12-11. Grasping by parallel shape matching. https://arxiv.org/abs/2412.08346

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