  Bed-making is a universal home task that can be challenging for senior
citizens due to reaching motions. Automating bed-making has multiple technical
challenges such as perception in an unstructured environments, deformable
object manipulation, obstacle avoidance and sequential decision making. We
explore how DART, an LfD algorithm for learning robust policies, can be applied
to automating bed making without fiducial markers with a Toyota Human Support
Robot (HSR). By gathering human demonstrations for grasping the sheet and
failure detection, we can learn deep neural network policies that leverage
pre-trained YOLO features to automate the task. Experiments with a 1/2 scale
twin bed and distractors placed on the bed, suggest policies learned on 50
demonstrations with DART achieve 96% sheet coverage, which is over 200% better
than a corner detector baseline using contour detection.
