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，通过机器人进行袋子操作复杂且具有挑战性。基于动态操作策略，我们提出了一种新框架ShakingBot，用于完成装袋任务。ShakingBot利用感知模块从任意初始配置中识别塑料袋的关键区域。根据分割结果，ShakingBot迭代执行一组新颖动作，包括袋子调整、双臂抖动和单臂抓持，以打开袋子。动态动作“双臂抖动”能够有效打开袋子，无需考虑其皱缩配置。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，在多种初始袋子配置下，成功插入至少一件物品的成功率达到21/33。在本工作中，我们展示了动态抖动动作相比准静态操作在装袋任务中的性能优势。同时证明，尽管袋子的尺寸、图案和颜色存在差异，我们的方法仍能泛化至不同情形。