  Nowadays service robots are entering more and more in our daily life. In such
a dynamic environment, a robot frequently faces pile, packed, or isolated
objects. Therefore, it is necessary for the robot to know how to grasp and
manipulate various objects in different situations to help humans in everyday
tasks. Most state-of-the-art grasping approaches addressed four
degrees-of-freedom (DoF) object grasping, where the robot is forced to grasp
objects from above based on grasp synthesis of a given top-down scene. Although
such approaches showed a very good performance in predefined industrial
settings, they are not suitable for human-centric environments as the robot
will not able to grasp a range of household objects robustly, for example,
grasping a bottle from above is not stable. In this work, we propose a
multi-view deep learning approach to handle robust object grasping in
human-centric domains. In particular, our approach takes a partial point cloud
of a scene as an input, and then, generates multi-views of existing objects.
The obtained views of each object are used to estimate pixel-wise grasp
synthesis for each object. To evaluate the performance of the proposed
approach, we performed extensive experiments in both simulation and real-world
environments within the pile, packed, and isolated objects scenarios.
Experimental results showed that our approach can estimate appropriate grasp
configurations in only 22ms without the need for explicit collision checking.
Therefore, the proposed approach can be used in real-time robotic applications
that need closed-loop grasp planning.
