  This paper tackles the task of goal-conditioned dynamic manipulation of
deformable objects. This task is highly challenging due to its complex dynamics
(introduced by object deformation and high-speed action) and strict task
requirements (defined by a precise goal specification). To address these
challenges, we present Iterative Residual Policy (IRP), a general learning
framework applicable to repeatable tasks with complex dynamics. IRP learns an
implicit policy via delta dynamics -- instead of modeling the entire dynamical
system and inferring actions from that model, IRP learns delta dynamics that
predict the effects of delta action on the previously-observed trajectory. When
combined with adaptive action sampling, the system can quickly optimize its
actions online to reach a specified goal. We demonstrate the effectiveness of
IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to
reach a target pose. Despite being trained only in simulation on a fixed robot
setup, IRP is able to efficiently generalize to noisy real-world dynamics, new
objects with unseen physical properties, and even different robot hardware
embodiments, demonstrating its excellent generalization capability relative to
alternative approaches. Video is available at https://youtu.be/7h3SZ3La-oA
