  Robotic arm grasping is a fundamental operation in robotic control task
goals. Most current methods for robotic grasping focus on RGB-D policy in the
table surface scenario or 3D point cloud analysis and inference in the 3D
space. Comparing to these methods, we propose a novel real-time multimodal
hierarchical encoder-decoder neural network that fuses RGB and depth data to
realize robotic humanoid grasping in 3D space with only partial observation.
The quantification of raw depth data's uncertainty and depth estimation fusing
RGB is considered. We develop a general labeling method to label ground-truth
on common RGB-D datasets. We evaluate the effectiveness and performance of our
method on a physical robot setup and our method achieves over 90\% success rate
in both table surface and 3D space scenarios.
