A two-stage optimization pipeline produces 9.5 million validated grasps across 31 GRASP taxonomy types and enables a type-conditional generative model to grasp novel objects from single-view point clouds with 82.3% real-world success.
Cross-Category Functional Grasp Transfer
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Generating grasps for a dexterous hand often requires numerous grasping annotations. However, annotating high DoF dexterous hand poses is quite challenging. Especially for functional grasps, requiring the hand to grasp the object in a specific pose to facilitate subsequent manipulations. This prompts us to explore how people achieve manipulations on new objects based on past grasp experiences. We find that when grasping new items, people are adept at discovering and leveraging various similarities between objects, including shape, layout, and grasp type. Considering this, we analyze and collect grasp-related similarity relationships among 51 common tool-like object categories and annotate semantic grasp representation for 1768 objects. These objects are connected through similarities to form a knowledge graph, which helps infer our proposed cross-category functional grasp synthesis. Through extensive experiments, we demonstrate that the grasp-related knowledge indeed contributed to achieving functional grasp transfer across unknown or entirely new categories of objects.
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy
A two-stage optimization pipeline produces 9.5 million validated grasps across 31 GRASP taxonomy types and enables a type-conditional generative model to grasp novel objects from single-view point clouds with 82.3% real-world success.