  In this paper, we propose a deep reinforcement learning (DRL) solution to the
grasping problem using 2.5D images as the only source of information. In
particular, we developed a simulated environment where a robot equipped with a
vacuum gripper has the aim of reaching blocks with planar surfaces. These
blocks can have different dimensions, shapes, position and orientation. Unity
3D allowed us to simulate a real-world setup, where a depth camera is placed in
a fixed position and the stream of images is used by our policy network to
learn how to solve the task. We explored different DRL algorithms and problem
configurations. The experiments demonstrated the effectiveness of the proposed
DRL algorithm applied to grasp tasks guided by visual depth camera inputs. When
using the proper policy, the proposed method estimates a robot tool
configuration that reaches the object surface with negligible position and
orientation errors. This is, to the best of our knowledge, the first successful
attempt of using 2.5D images only as of the input of a DRL algorithm, to solve
the grasping problem regressing 3D world coordinates.
