RaggeDi: Diffusion-based State Estimation of Disordered Rags, Sheets, Towels and Blankets

Jikai Ye, Wanze Li, Shiraz Khan, Gregory S. Chirikjian
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Abstract

Cloth state estimation is an important problem in robotics. It is essential for the robot to know the accurate state to manipulate cloth and execute tasks such as robotic dressing, stitching, and covering/uncovering human beings. However, estimating cloth state accurately remains challenging due to its high flexibility and self-occlusion. This paper proposes a diffusion model-based pipeline that formulates the cloth state estimation as an image generation problem by representing the cloth state as an RGB image that describes the point-wise translation (translation map) between a pre-defined flattened mesh and the deformed mesh in a canonical space. Then we train a conditional diffusion-based image generation model to predict the translation map based on an observation. Experiments are conducted in both simulation and the real world to validate the performance of our method. Results indicate that our method outperforms two recent methods in both accuracy and speed.
RaggeDi:基于扩散的无序抹布、床单、毛巾和毯子的状态估计
布料状态估计是机器人技术中的一个重要问题。机器人要想操控布料并执行任务,如机器人穿衣、缝合和为人类盖/脱衣服等,就必须知道布料的准确状态。然而,由于布料的高柔韧性和自闭性,准确估计布料状态仍具有挑战性。本文提出了一种基于扩散模型的管道,它将布料状态估算表述为图像生成问题,将布料状态表示为 RGB 图像,该图像描述了预定义的扁平化网格与典型空间中的变形网格之间的随点平移(平移图)。然后,我们训练一个基于条件扩散的图像生成模型,根据观测结果预测平移图。我们在模拟和现实世界中进行了实验,以验证我们方法的性能。结果表明,我们的方法在准确性和速度上都优于最近的两种方法。
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