In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interference resolution and quilt spreading. By leveraging the DWPose human skeletal detection and the Segment Anything instance segmentation models, the proposed method can accurately recognize the states of the infant and the quilt over her, which involves addressing the interferences resulted from an infant's limbs laid on part of the quilt. Building upon prior research, the EM*D deep learning model is employed to forecast quilt state transitions before and after quilt spreading actions. To improve the sensitivity of the network in distinguishing state variation of the handled quilt, we introduce an enhanced loss function that translates the voxelized quilt state into a more representative one. Both simulation and real-world experiments validate the efficacy of our method, in spreading and recover a quilt over an infant.

翻译：本研究提出了一种创新策略，旨在确保睡眠中无意蹬被的婴儿能够被及时、准确地重新覆盖。我们的方法被构建为两个连续步骤：干扰消除与被子铺展。通过利用DWPose人体骨骼检测模型与Segment Anything实例分割模型，所提方法能够精准识别婴儿及其身上被子的状态，这包括处理因婴儿肢体部分压住被子而产生的干扰问题。基于先前研究，我们采用EM*D深度学习模型来预测铺被动作前后被子状态的转变。为增强网络对处理过程中被子状态变化的辨识灵敏度，我们引入了一种改进的损失函数，将体素化的被子状态转换为更具表征性的形式。仿真实验与真实场景实验均验证了本方法在婴儿铺被与覆盖任务中的有效性。