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:袋装处理任务对于机器人的操纵是复杂而具有挑战性的,这是由于塑料袋的可变形性引起的。基于动态操纵策略,我们提出了一个新的框架,ShakingBot,用于袋装任务。ShakingBot利用一个感知模块来从任意的初始配置中识别塑料袋的关键区域。根据分割,ShakingBot迭代执行一组新的动作,包括袋子调整、双臂震动和单臂持握,以打开袋子。动态动作——双臂震动可以在不考虑袋子皱褶状态的情况下有效地打开袋子。然后,我们将物品插入并举起袋子进行运输。我们在双臂机器人上进行了我们的方法,并在各种初始袋子配置下获得了不少于一件物品的插入成功率为21/33。在这项工作中,我们证明了相对于袋装任务中准静态操纵的动态震动动作的优越性能。我们还展示了我们的方法在尺寸、图案和颜色方面的变化中具有泛化性。