  Bag manipulation through robots is complex and challenging due to the
deformability of the bag. Based on dynamic manipulation strategy, we propose a
new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a
perception module to identify the key region of the plastic bag from arbitrary
initial configurations. According to the segmentation, ShakingBot iteratively
executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking,
and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can
effectively open the bag without the need to account for the crumpled
configuration.Then, we insert the items and lift the bag for transport. We
perform our method on a dual-arm robot and achieve a success rate of 21/33 for
inserting at least one item across various initial bag configurations. In this
work, we demonstrate the performance of dynamic shaking actions compared to the
quasi-static manipulation in the bagging task. We also show that our method
generalizes to variations despite the bag's size, pattern, and color.
