In the realm of robotic cloth manipulation, accurately estimating the cloth state during or post-execution is imperative. However, the inherent complexities in a cloth's dynamic behavior and its near-infinite degrees of freedom (DoF) pose significant challenges. Traditional methods have been restricted to using keypoints or boundaries as cues for cloth state, which do not holistically capture the cloth's structure, especially during intricate tasks like folding. Additionally, the critical influence of cloth physics has often been overlooked in past research. Addressing these concerns, we introduce DiffCP, a novel differentiable pipeline that leverages the Anisotropic Elasto-Plastic (A-EP) constitutive model, tailored for differentiable computation and robotic tasks. DiffCP adopts a ``real-to-sim-to-real'' methodology. By observing real-world cloth states through an RGB-D camera and projecting this data into a differentiable simulator, the system identifies physics parameters by minimizing the geometric variance between observed and target states. Extensive experiments demonstrate DiffCP's ability and stability to determine physics parameters under varying manipulations, grasping points, and speeds. Additionally, its applications extend to cloth material identification, manipulation trajectory generation, and more notably, enhancing cloth pose estimation accuracy. More experiments and videos can be found in the supplementary materials and on the website: https://sites.google.com/view/diffcp.

翻译：在机器人布料操作领域，精确估计操作过程中或操作后的布料状态至关重要。然而，布料动态行为的固有复杂性及其近乎无限的自由度带来了重大挑战。传统方法仅限于使用关键点或边界作为布料状态的线索，无法全面捕捉布料的结构特性，尤其是在折叠等复杂任务中。此外，过去的研究常常忽视布料物理特性的关键影响。针对这些问题，我们提出了DiffCP——一种新颖的可微分流水线，它采用面向可微计算和机器人任务定制的各向异性弹塑性（A-EP）本构模型。DiffCP采用"真实-仿真-真实"的方法论。通过RGB-D相机观测真实布料状态，并将数据投影到可微分仿真器中，系统通过最小化观测状态与目标状态之间的几何差异来辨识物理参数。大量实验表明，DiffCP能够在不同操作方式、抓取点和速度下稳定地确定物理参数。此外，其应用还可扩展至布料材料识别、操作轨迹生成，尤其在提升布料位姿估计精度方面表现突出。更多实验和视频详见补充材料及网站：https://sites.google.com/view/diffcp。