  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. Our
code is available at https://github.com/yanjh97/TransGrasp.
