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.

翻译：由于塑料袋的可变形性，机器人对其进行操作复杂且富有挑战性。基于动态操作策略，我们提出了一种名为ShakingBot的新框架，用于完成装袋任务。ShakingBot利用感知模块从任意初始构型中识别塑料袋的关键区域。根据分割结果，ShakingBot迭代执行一组新颖的动作，包括袋子调整、双臂抖动和单臂持握，以打开袋子。动态动作“双臂抖动”可有效打开袋子，而无需考虑其褶皱构型。随后，我们放入物品并提起袋子进行搬运。我们在双臂机器人上实施该方法，并在各种初始袋子构型下实现了21/33的至少放入一件物品的成功率。本工作中，我们展示了动态抖动动作相较于准静态操作在装袋任务中的性能表现。我们还证明了该方法能够泛化到不同尺寸、图案和颜色的袋子变体。