  As robots become more widely available outside industrial settings, the need
for reliable object grasping and manipulation is increasing. In such
environments, robots must be able to grasp and manipulate novel objects in
various situations. This paper presents GraspCaps, a novel architecture based
on Capsule Networks for generating per-point 6D grasp configurations for
familiar objects. GraspCaps extracts a rich feature vector of the objects
present in the point cloud input, which is then used to generate per-point
grasp vectors. This approach allows the network to learn specific grasping
strategies for each object category. In addition to GraspCaps, the paper also
presents a method for generating a large object-grasping dataset using
simulated annealing. The obtained dataset is then used to train the GraspCaps
network. Through extensive experiments, we evaluate the performance of the
proposed approach, particularly in terms of the success rate of grasping
familiar objects in challenging real and simulated scenarios. The experimental
results showed that the overall object-grasping performance of the proposed
approach is significantly better than the selected baseline. This superior
performance highlights the effectiveness of the GraspCaps in achieving
successful object grasping across various scenarios.
