# Generalized Anthropomorphic Functional Grasping with Minimal Demonstrations

arXiv: https://arxiv.org/abs/2303.17808 · HTML: https://mlsanity.com/paper/2303.17808/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation](https://mlsanity.com/paper/2310.16838/) (2023)
- [PICO: Reconstructing 3D People In Contact with Objects](https://mlsanity.com/paper/2504.17695/) (2025)
- [Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping](https://mlsanity.com/paper/2410.02475/) (2024)
- [Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping](https://mlsanity.com/paper/2410.23039/) (2024)
- [Anthropomorphic Grasping with Neural Object Shape Completion](https://mlsanity.com/paper/2311.02510/) (2023)
- [Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy](https://mlsanity.com/paper/2403.12421/) (2024)
- [FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance](https://mlsanity.com/paper/2502.20805/) (2025)

## Abstract (arXiv)

This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use. However, accomplishing human-like grasping in real robots present many challenges, including obtaining diverse functional grasps for a wide variety of objects, handling generalization ability for kinematically diverse robot hands and precisely completing object shapes from a single-view perception. To tackle these challenges, we propose a six-step grasp synthesis algorithm based on fine-grained contact modeling that generates physically plausible and human-like functional grasps for category-level objects with minimal human demonstrations. With the contact-based optimization and learned dense shape correspondence, the proposed algorithm is adaptable to various objects in same category and a board range of robot hand models. To further demonstrate the robustness of the framework, over 10K functional grasps are synthesized to train our neural network, named DexFG-Net, which generates diverse sets of human-like functional grasps based on the reconstructed object model produced by a shape completion module. The proposed framework is extensively validated in simulation and on a real robot platform. Simulation experiments demonstrate that our method outperforms baseline methods by a large margin in terms of grasp functionality and success rate. Real robot experiments show that our method achieved an overall success rate of 79% and 68% for tool-use grasp on 3-D printed and real test objects, respectively, using a 5-Finger Schunk Hand. The experimental results indicate a step towards human-like grasping with anthropomorphic hands.