Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp pose estimation method that predicts grasp poses of a category of objects by labeling only one object instance. Specifically, we perform grasp pose transfer across a category of objects based on their shape correspondences and propose a grasp pose refinement module to further fine-tune grasp pose of grippers so as to ensure successful grasps. Experiments demonstrate the effectiveness of our method on achieving high-quality grasps with the transferred grasp poses.

Overview of the proposed TransGrasp

If you find this work useful, please consider citing our work as follows:

@inproceedings{wen2022transgrasp,

  title={Transgrasp: Grasp pose estimation of a category of objects by transferring grasps from only one labeled instance},

  author={Wen, Hongtao and Yan, Jianhang and Peng, Wanli and Sun, Yi},

  booktitle={European Conference on Computer Vision},

  pages={445-461},

  year={2022},

  organization={Springer}

}

}