  Fabric manipulation dynamically is commonly seen in manufacturing and
domestic settings. While dynamically manipulating a fabric piece to reach a
target state is highly efficient, this task presents considerable challenges
due to the varying properties of different fabrics, complex dynamics when
interacting with environments, and meeting required goal conditions. To address
these challenges, we present \textit{One Fling to Goal}, an algorithm capable
of handling fabric pieces with diverse shapes and physical properties across
various scenarios. Our method learns a graph-based dynamics model equipped with
environmental awareness. With this dynamics model, we devise a real-time
controller to enable high-speed fabric manipulation in one attempt, requiring
less than 3 seconds to finish the goal-conditioned task. We experimentally
validate our method on a goal-conditioned manipulation task in five diverse
scenarios. Our method significantly improves this goal-conditioned task,
achieving an average error of 13.2mm in complex scenarios. Our method can be
seamlessly transferred to real-world robotic systems and generalized to unseen
scenarios in a zero-shot manner.
