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, accurately estimating the cloth state remains challenging due to the high flexibility and self-occlusion of cloth. 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 predefined 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. More results and code are available on our project website: https://chirikjianlab.github.io/RaggeDi/

Malaria is a worldwide scourge, and the broad deployment of an effective vaccine would improve the lives of millions of people. A vaccine based on Plasmodium falciparum (PfSPZ) sporozoites extracted from the salivary glands of infected mosquitoes shows significant promise. However, the large-scale industrial production of PfSPZ-based vaccines will benefit from automation of the key step of extracting sporozoites from mosquito salivary glands that is currently performed by manual microdissection. In this work, we demonstrate a robotic system prototype for extracting salivary glands from mosquitoes to streamline vaccine production and reduce the need for operators. In the proposed system, mosquitoes are decapitated in an automated robotic pick-place-decapitate process, then a squeezer apparatus extracts mosquito salivary glands from the body. Mosquito detection and body part localization are performed by computer vision methods. The software allows system operation in simulation and on the robotic hardware, which facilitates subsystem development and integration. Experiments show encouraging results with success rates of 93% in robotic mosquito manipulation and 87.1% in salivary gland extraction. The system has the potential to improve the efficiency of PfSPZ vaccine production with significant gains in throughput and reduction in training times for a highly deskilled initial manual step. Further, this system is expected to pave the way for a more mature future system.