用扩散模型把衣服状态估计成图像生成任务，提升精度与速度
RaggeDi: Diffusion-based State Estimation of Disordered Rags, Sheets, Towels and Blankets
- 将衣物状态表示为点对点位移图，转化为图像生成问题
- 在仿真和真实场景中均优于现有方法，精度与速度双提升
- 适合需精准抓取衣物的机器人应用，如穿脱、铺盖
衣物状态估计是机器人领域的重要挑战。准确掌握衣物状态对于执行穿脱、缝纫及覆盖/揭开人体等任务至关重要，但高柔性和自遮挡使得估计困难。本文提出一种基于扩散模型的流水线，将衣物状态估计建模为图像生成问题：将衣物状态表示为一张RGB图像，描述预定义的展开网格与规范空间中变形网格之间的逐点位移（位移图）。随后训练一个条件扩散图像生成模型，根据观测数据预测该位移图。在仿真与真实世界中均进行了实验验证，结果表明，本方法在准确率与速度上均优于两种近期方法。
原文摘要 · Abstract (English)
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.
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