Learning Single-Arm Fling Motions: Efficient Garment Smoothing

This lecture presents a study on learning single-arm fling motions to smooth garments efficiently. The instructor discusses the challenges faced by the fashion industry in garment smoothing and the complex configuration space involved. Various strategies, such as garment smoothing with quasi-static and dynamic actions, are explored. The lecture delves into types of uncertainty, including epistemic and aleatoric uncertainty, and their impact on single-arm fling repeatability. Additionally, optimization techniques like Coarse-to-Fine Optimization and leveraging garment category priors for accelerated learning are discussed. The lecture also covers the concept of optimal stopping rules during execution and presents results from physical experiments comparing robot performance to humans.