  Recent work has shown that 2-arm "fling" motions can be effective for garment
smoothing. We consider single-arm fling motions. Unlike 2-arm fling motions,
which require little robot trajectory parameter tuning, single-arm fling
motions are very sensitive to trajectory parameters. We consider a single 6-DOF
robot arm that learns fling trajectories to achieve high garment coverage.
Given a garment grasp point, the robot explores different parameterized fling
trajectories in physical experiments. To improve learning efficiency, we
propose a coarse-to-fine learning method that first uses a multi-armed bandit
(MAB) framework to efficiently find a candidate fling action, which it then
refines via a continuous optimization method. Further, we propose novel
training and execution-time stopping criteria based on fling outcome
uncertainty; the training-time stopping criterion increases data efficiency
while the execution-time stopping criteria leverage repeated fling actions to
increase performance. Compared to baselines, the proposed method significantly
accelerates learning. Moreover, with prior experience on similar garments
collected through self-supervision, the MAB learning time for a new garment is
reduced by up to 87%. We evaluate on 36 real garments: towels, T-shirts,
long-sleeve shirts, dresses, sweat pants, and jeans. Results suggest that using
prior experience, a robot requires under 30 minutes to learn a fling action for
a novel garment that achieves 60-94% coverage.
