Differentiable Cloth Parameter Identification and State Estimation in Manipulation

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.