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：机器人对塑料袋的操作因其可变形特性而复杂且极具挑战性。基于动态操作策略，我们提出了一种用于装袋任务的新框架——ShakingBot。该框架通过感知模块识别塑料袋从任意初始构型中的关键区域。根据分割结果，ShakingBot迭代执行一组新型动作（包括袋调整、双臂抖动和单臂握持）以打开袋口。其中动态动作"双臂抖动"可在无需考虑皱褶构型的情况下有效打开袋口。随后我们装入物品并提起塑料袋进行运输。我们在双臂机器人平台上实施该方法，在多种初始袋构型条件下，成功将至少一个物品插入袋中的成功率达21/33。本工作展示了装袋任务中动态抖动操作相对于准静态操作的性能优势，并证明该方法对塑料袋的尺寸、图案和颜色变化具有泛化能力。