  Bagging is an essential skill that humans perform in their daily activities.
However, deformable objects, such as bags, are complex for robots to
manipulate. This paper presents an efficient learning-based framework that
enables robots to learn bagging. The novelty of this framework is its ability
to perform bagging without relying on simulations. The learning process is
accomplished through a reinforcement learning algorithm introduced in this
work, designed to find the best grasping points of the bag based on a set of
compact state representations. The framework utilizes a set of primitive
actions and represents the task in five states. In our experiments, the
framework reaches a 60 % and 80 % of success rate after around three hours of
training in the real world when starting the bagging task from folded and
unfolded, respectively. Finally, we test the trained model with two more bags
of different sizes to evaluate its generalizability.
