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 Trans-Grasp, 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.

Nvidia Research has been making strides in using deep learning to train models for various tasks. Recently, the company clocked the fastest training times for BERT and trained the largest ever transformer-based model. However, as expected, algorithms based on deep learning require a large dataset to begin with, and that is a luxury in many situations. Along with continuing research using deep learning, the company focused its efforts in another direction as well. And the firm, at its Seattle Robotics Lab, developed a novel algorithm, called 6-DoF GraspNet, that allows robots to grasp arbitrary objects.