Weakly supervised 6D pose estimation for robotic grasping.

Learning based robotic grasping methods achieve substantial progress with the development of the deep neural networks. However, the requirement of large-scale training data in the real world limits the application scopes of these methods. Given the 3D models of the target objects, we propose a new learning-based grasping approach built on 6D object poses estimation from a monocular RGB image. We aim to leverage both a large-scale synthesized 6D object pose dataset and a small scale of the real-world weakly labeled dataset (e.g., mark the number of objects in the image), to reduce the system deployment difficulty. In particular, the deep network combines the 6D pose estimation task and an auxiliary task of weak labels to perform knowledge transfer between the synthesized and real-world data. We demonstrate the effectiveness of the method in a real robotic environment and show substantial improvements in the successful grasping rate (about 11.9% on average) to the proposed knowledge transfer scheme.