HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models
Dehao Huang1,2, Wenlong Dong1,2, Chao Tang1,2, Hong Zhang1,2
[1] Shenzhen Key Laboratory of Robotics and Computer Vision, SUSTech, Shenzhen, China.
[2] Department of Electronic and Electrical Engineering, SUSTech, Shenzhen, China.
Abstract: Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans naturally grasp objects in a task-oriented manner to facilitate subsequent manipulation tasks. By leveraging human grasp demonstrations, current methods can generate high-quality robotic parallel-jaw task-oriented grasps for diverse objects and tasks. However, they still encounter challenges in maintaining grasp stability and sampling efficiency. These methods typically rely on a two-stage process: first performing exhaustive task-agnostic grasp sampling in the 6-DoF space, then applying demonstration-induced constraints (e.g., contact regions and wrist orientations) to filter candidates. This leads to inefficiency and potential failure due to the vast sampling space. To address this, we propose the Human-guided Grasp Diffuser (HGDiffuser), a diffusion-based framework that integrates these constraints into a guided sampling process. Through this approach, HGDiffuser directly generates 6-DoF task-oriented grasps in a single stage, eliminating exhaustive task-agnostic sampling. Furthermore, by incorporating Diffusion Transformer (DiT) blocks as the feature backbone, HGDiffuser improves grasp generation quality compared to MLP-based methods. Experimental results demonstrate that our approach significantly improves the efficiency of task-oriented grasp generation, enabling more effective transfer of human grasping strategies to robotic systems.
Overview:
(a) Demonstration-based methods. (b) Comparison of existing two-stage methods and our single-stage method.
Supplementary Video:
Pipeline:
Overview of Our TOG system
Overview of HGDiffuser
Quantitative Results:
Quantitative results: In the bottom-right section, we compare our method with baseline approaches in terms of average task-oriented grasping success rate and average inference time. The remaining sections present the average success rates across 24 object categories (out of 236 total object instances) from the dataset.
Qualitative Results:
Qualitative results: Qualitative results of our method and Ours-TS method. The object categories and tasks are as follows: (a) toothbrush and brushing, (b) wine glass and pouring, (c) eyeglasses and handing over, (d) scissors and using. More results are provided in the supplementary material.
Authors:
Citation:
@article{huang2025hgdiffuser,
title={HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models},
author={Huang, Dehao and Dong, Wenlong and Tang, Chao and Zhang, Hong},
journal={arXiv preprint arXiv:2503.00508},
year={2025}
}
