RaggeDi: Diffusion-based State Estimation of Disordered Rags, Sheets, Towels and Blankets
This addresses the problem of accurately estimating flexible cloth states for robotic tasks like dressing and covering, though it appears incremental as it builds on existing diffusion models for a specific domain.
The paper tackles cloth state estimation for robotics by proposing a diffusion model-based pipeline that formulates it as an image generation problem, representing cloth state as an RGB translation map. Results show the method outperforms two recent approaches in both accuracy and speed in simulation and real-world experiments.
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.