# Cross-Category Functional Grasp Transfer

arXiv: https://arxiv.org/abs/2405.08310 · HTML: https://mlsanity.com/paper/2405.08310/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2024.

## Built on by (most cited first, 9 of 9)

- [Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy](https://mlsanity.com/paper/2504.18829/) (2025)
- [DexVLG: Dexterous Vision-Language-Grasp Model at Scale](https://mlsanity.com/paper/2507.02747/) (2025)
- [Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning](https://mlsanity.com/paper/2512.13380/) (2025)
- [DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning](https://mlsanity.com/paper/2601.16046/) (2026)
- [RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer](https://mlsanity.com/paper/2409.14519/) (2024)
- [Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation](https://mlsanity.com/paper/2511.01276/) (2025)
- [OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance](https://mlsanity.com/paper/2512.03874/) (2025)
- [DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2505.14819/) (2025)
- [CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning](https://mlsanity.com/paper/2608.19776/) (2026)

## Abstract (arXiv)

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.