2.5D Image-based Robotic Grasping

Abstract

We consider the problem of robotic grasping by 2. 5D image data sampling from a real sensor. We design an encoder-decoder neural network to predict grasping policy in real-time which enhances the robustness for the policy generation at different observation heights by fusing depth image and RGB image. We propose an open-loop algorithm to realize robotic grasp operation and evaluate our method in a physical robotic system. The result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0.