Placing fabric poses a challenge to robots since fabric with high dimensional configuration space can deform during manipulation. Existing methods for placing fabric mostly rely on static operations, which are inefficient and require a large workspace. Therefore, this study applies dynamic manipulation (manipulating uncontrollable parts of the fabric by swinging) to fabric placement, proposing a novel learning framework for robotic dynamic fabric placement skill learning and generalization. The proposed framework integrates reinforcement learning with imitation learning, leveraging expert demonstration data to guide and accelerate skill acquisition. Additionally, fabric characteristics are combined with imitation learning to enable the transfer and generalization of the learned policy to real-world environments The experiments suggest that the proposed framework is capable of achieving the placement tasks for a range of positions and fabrics. For success rate, the policy of the proposed framework ultimately achieves a flatness of exceeding 95% and a placement distance error of less than 2 mm. Moreover, the proposed approach is similar in operation time to the fastest method, while it can reduce the space required for manipulating the fabric by over 15%. Compared with other placement policies, it is promising because of its high accuracy, flexibility, efficiency, as well as adaptability.

中文翻译：

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一种用于织物放置的动态操作机器人技能迁移学习框架


放置织物对机器人构成挑战，因为具有高维配置空间的织物在操作过程中可能会变形。现有的放置织物的方法大多依赖于静态操作，这些操作效率低下且需要较大的工作空间。因此，本研究将动态操作（通过摆动操纵织物的不可控部分）应用于织物放置，提出了一种新的机器人动态织物放置技能学习和泛化学习框架。拟议的框架将强化学习与模仿学习相结合，利用专家演示数据来指导和加速技能获取。此外，织物特性与模仿学习相结合，使学习到的策略能够转移和泛化到现实世界的环境中。实验表明，所提出的框架能够完成一系列位置和织物的放置任务。对于成功率，所提出的框架策略最终实现了超过 95% 的平面度和小于 2 mm 的放置距离误差。此外，所提出的方法在操作时间上与最快的方法相似，同时它可以将处理织物所需的空间减少 15% 以上。与其他放置策略相比，它因其高精度、灵活性、效率和适应性而备受期待。