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Robotic caregivers could potentially improve the quality of life of many whorequire physical assistance. However, in order to assist individuals who arelying 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. Wepropose a method for targeted bedding manipulation over people lying supine inbed where we first learn a model of the cloth's dynamics. Then, we optimizeover this model to uncover a given target limb using information about humanbody shape and pose that only needs to be provided at run-time. We show howthis approach enables greater robustness to variation relative to geometric andreinforcement learning baselines via a number of generalization evaluations insimulation and in the real world. We further evaluate our approach in a humanstudy with 12 participants where we demonstrate that a mobile manipulator canadapt to real variation in human body shape, size, pose, and blanketconfiguration to uncover target body parts without exposing the rest of thebody. Source code and supplementary materials are available online.

Robust Body Exposure (RoBE): A Graph-based Dynamics Modeling Approach to Manipulating Blankets over People