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

التفاصيل البيبلوغرافية
العنوان: TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance
المؤلفون: Wen, Hongtao, Yan, Jianhang, Peng, Wanli, Sun, Yi
سنة النشر: 2022
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
الوصف: 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.
Comment: Accepted to European Conference on Computer Vision (ECCV) 2022
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2207.07861
رقم الأكسشن: edsarx.2207.07861
قاعدة البيانات: arXiv