PartGrasp: Generalizable Part-level Grasping via Semantic-Geometric Alignment

Abstract

The ability to perform generalizable and precise grasping on functional object parts is a prerequisite for robotic manipulation in open environments. Recent foundation models have demonstrated promising semantic correspondence capabilities in guiding robots to grasp similar parts across objects with resembling shapes and poses. However, existing works struggle to generalize precise grasp poses when the target objects exhibit substantial geometric and positional variations. To tackle this challenge, we present PartGrasp, a method that achieves precise part grasping through hierarchical integration of highly generalizable semantic correspondence and precise geometric registration. Specifically, we first build a grasp knowledge bank by extracting grasp poses and object meshes from demonstrations. Upon retrieving a reference from this bank, we initially perform a coarse alignment using semantic correspondence, followed by a fine registration that adapts to geometric variations. This approach achieves fine-grained generalization of part grasping that is robust to both shape and pose variations. Extensive experiments demonstrate the efficacy of our method in terms of both generalization capability and accuracy. Videos and more details are available on our project site: https://part-grasp.github.io/partgrasp/.