  Thin plastic bags are ubiquitous in retail stores, healthcare, food handling,
recycling, homes, and school lunchrooms. They are challenging both for
perception (due to specularities and occlusions) and for manipulation (due to
the dynamics of their 3D deformable structure). We formulate the task of
"bagging:" manipulating common plastic shopping bags with two handles from an
unstructured initial state to an open state where at least one solid object can
be inserted into the bag and lifted for transport. We propose a self-supervised
learning framework where a dual-arm robot learns to recognize the handles and
rim of plastic bags using UV-fluorescent markings; at execution time, the robot
does not use UV markings or UV light. We propose the AutoBag algorithm, where
the robot uses the learned perception model to open a plastic bag through
iterative manipulation. We present novel metrics to evaluate the quality of a
bag state and new motion primitives for reorienting and opening bags based on
visual observations. In physical experiments, a YuMi robot using AutoBag is
able to open bags and achieve a success rate of 16/30 for inserting at least
one item across a variety of initial bag configurations. Supplementary material
is available at https://sites.google.com/view/autobag.
