  Robotic caregivers could potentially improve the quality of life of many who
require physical assistance. However, in order to assist individuals who are
lying in bed, robots must be capable of dealing with a significant obstacle:
the blanket or sheet that will almost always cover the person's body. We
propose a method for targeted bedding manipulation over people lying supine in
bed where we first learn a model of the cloth's dynamics. Then, we optimize
over this model to uncover a given target limb using information about human
body shape and pose that only needs to be provided at run-time. We show how
this approach enables greater robustness to variation relative to geometric and
reinforcement learning baselines via a number of generalization evaluations in
simulation and in the real world. We further evaluate our approach in a human
study with 12 participants where we demonstrate that a mobile manipulator can
adapt to real variation in human body shape, size, pose, and blanket
configuration to uncover target body parts without exposing the rest of the
body. Source code and supplementary materials are available online.
