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:机器人养护员可以有效提升许多需要身体协助的人的生活质量。然而,要协助躺在床上的人,机器人必须能够处理一个重要的障碍:几乎总是会覆盖着人体的被子或床单。我们提出了一种定点操作人体覆盖物的方法,首先学习布料的动力学模型。然后,我们在这个模型上进行优化,来揭示只需要在运行时提供的关于人体形状、姿势的信息,以达到揭露目标肢体的目的。我们通过在模拟和实际环境下进行多项泛化评估,展示了这种基于图像动态建模方法相比于几何和强化学习基线的更高的稳健性。我们在12个参与者的人体研究中进一步评估了我们的方法,证明了移动操纵器可以适应人体形状、大小、姿势和被单配置的实际变化,不会暴露余下的身体部位。源代码和补充材料可在网上获取。