Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these primitives. Although existing solutions have tackled the parameterisation of pick and place locations, the effect of factors such as the velocity or trajectory of quasi-static and dynamic manipulation primitives has been neglected. Choosing appropriate values for these parameters is crucial to cope with the range of materials present in house-hold cloth objects. To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives. In this work, we leverage the framework of Sequential Reinforcement Learning to decouple sequentially the parameters that compose the primitives. To evaluate the effectiveness of the method we focus on the task of cloth unfolding with a robotic arm in simulation and real-world experiments. Our results in simulation show that by deciding the optimal parameters for the primitives the performance can improve by 20% compared to sub-optimal ones. Real-world results demonstrate the advantage of modifying the velocity and height of manipulation primitives for cloths with different mass, stiffness, shape and size. Supplementary material, videos, and code, can be found at https://sites.google.com/view/qdp-srl.

翻译：摘要：预定义的操作基元广泛应用于布料操作中。然而，布料的刚度或密度等属性会显著影响这些基元的性能。尽管现有解决方案已解决了抓取和放置位置的参数化问题，但诸如准静态和动态操作基元的速度或轨迹等因素的影响却被忽视了。为应对家居布料物体中存在的各种材料，选择这些参数的适当值至关重要。针对这一挑战，我们提出了准动态可参数化（QDP）方法，该方法除了优化准静态和动态操作基元的抓取和放置位置外，还优化了运动速度等参数。在本研究中，我们利用序列强化学习框架，依次解耦组成基元的参数。为评估该方法的有效性，我们聚焦于模拟和真实世界实验中用机械臂展开布料的任务。模拟结果表明，与次优参数相比，通过为基元选择最优参数，性能可提升20%。真实世界结果证明了根据布料的不同质量、刚度、形状和尺寸调整操作基元速度和高度的优势。补充材料、视频和代码可在 https://sites.google.com/view/qdp-srl 获取。