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Grasp pose estimation is an important issue for robots to interact with thereal world. However, most of existing methods require exact 3D object modelsavailable beforehand or a large amount of grasp annotations for training. Toavoid these problems, we propose TransGrasp, a category-level grasp poseestimation method that predicts grasp poses of a category of objects bylabeling only one object instance. Specifically, we perform grasp pose transferacross a category of objects based on their shape correspondences and propose agrasp pose refinement module to further fine-tune grasp pose of grippers so asto ensure successful grasps. Experiments demonstrate the effectiveness of ourmethod on achieving high-quality grasps with the transferred grasp poses. Ourcode is available at https://github.com/yanjh97/TransGrasp.

TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance