Over the last years, robotic cloth manipulation has gained relevance within the research community. While significant advances have been made in robotic manipulation of rigid objects, the manipulation of non-rigid objects such as cloth garments is still a challenging problem. The uncertainty on how cloth behaves often requires the use of model-based approaches. However, cloth models have a very high dimensionality. Therefore, it is difficult to find a middle point between providing a manipulator with a dynamics model of cloth and working with a state space of tractable dimensionality. For this reason, most cloth manipulation approaches in literature perform static or quasi-static manipulation. In this paper, we propose a variation of Gaussian Process Dynamical Models (GPDMs) to model cloth dynamics in a low-dimensional manifold. GPDMs project a high-dimensional state space into a smaller dimension latent space which is capable of keeping the dynamic properties. Using such approach, we add control variables to the original formulation. In this way, it is possible to take into account the robot commands exerted on the cloth dynamics. We call this new version Controlled Gaussian Process Dynamical Model (C-GPDM). Moreover, we propose an alternative kernel representation for the model, characterized by a richer parameterization than the one employed in the majority of previous GPDM realizations. The modeling capacity of our proposal has been tested in a simulated scenario, where C-GPDM proved to be capable of generalizing over a considerably wide range of movements and correctly predicting the cloth oscillations generated by previously unseen sequences of control actions.
翻译:过去几年来,机器人布的操纵在研究界中具有相关性。尽管在机械操纵僵硬物体方面取得了显著进步,但操纵布布服装等非硬性物体仍是一个具有挑战性的问题。布的行为的不确定性往往需要使用基于模型的方法。然而,布的模型具有非常高的维度。因此,很难找到一个中间点,介于提供带有布的动态模型的操纵器和与可移动的维度状态合作之间。为此原因,文献中的大多数布的操纵方法都采用静态或准静态操纵。在本文中,我们提议将高斯进程动态模型(GPDMs)的变换为低维度的布动模型(GPMs)。 GPDMs预测高维度空间为较小的维度潜伏空间,能够保持动态特性。因此,我们用这种方法将控控控变量添加到原始的构件中。我们称之为新版制高压的高压进程模型模型模型(CGPMDM)的模型(C-GPMDM)模型(C-MDM的精度模型)的变后,我们用了一个高度变的变的变的模型,我们用了一个高的变压的模型的变压的变压的变现的变现的变现的变数。