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.

翻译：机器人护理人员有望改善许多需要身体辅助者的生活质量。然而，为了协助卧床患者，机器人必须克服一个重大障碍：几乎总会覆盖人体的毯子或床单。我们提出了一种针对平卧患者的目标性床上用品操作方法，该方法首先学习布料动力学模型，然后通过该模型优化，利用仅在运行时提供的人体形状与姿态信息，暴露指定的目标肢体。我们通过仿真和真实环境中多项泛化评估实验证明：与几何基线和强化学习基线相比，该方法对变异具有更强的鲁棒性。进一步在包含12名参与者的人体实验研究中，我们验证了移动操作器能够适应人体形状、尺寸、姿态及毯子配置的真实变异，在未暴露身体其他部位的前提下成功暴露目标身体部位。源代码与补充材料已在线公开。