Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. This paper presents an efficient learning-based framework that enables robots to learn bagging. The novelty of this framework is its ability to perform bagging without relying on simulations. The learning process is accomplished through a reinforcement learning algorithm introduced in this work, designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilizes a set of primitive actions and represents the task in five states. In our experiments, the framework reaches a 60 % and 80 % of success rate after around three hours of training in the real world when starting the bagging task from folded and unfolded, respectively. Finally, we test the trained model with two more bags of different sizes to evaluate its generalizability.

翻译：装袋是人类日常活动中的一项基本技能。然而，对于机器人而言，袋子等可变形物体的操作具有较高复杂性。本文提出了一种高效的基于学习的框架，使机器人能够学习装袋操作。该框架的创新之处在于无需依赖仿真即可完成装袋任务。学习过程通过本文提出的强化学习算法实现，该算法基于一组紧凑的状态表征来搜索袋子的最优抓取点。框架采用一组基本动作，并将任务划分为五个状态。实验中，当分别以折叠和展开状态启动装袋任务时，框架经过约三小时真实环境训练后分别达到60%和80%的成功率。最后，我们使用两个不同尺寸的袋子对训练模型进行泛化性测试。