Large Scale Cloth

Research on large-scale cloth manipulation focuses on enabling robots to effectively interact with and manipulate fabrics like sheets and tablecloths, tasks challenging due to the material's deformability and complexity. Current efforts leverage neural networks, particularly diffusion models and regression networks, to predict cloth behavior and learn human-like strategies for tasks such as flattening, folding, and spreading. This work is crucial for advancing robotics in domestic and service applications, requiring the development of robust frameworks for characterizing cloth properties and implementing model-free control strategies based on visual and tactile feedback. Standardization of cloth properties and improved manipulation techniques are key to achieving reliable and efficient robotic handling of large-scale cloth.