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.

提出了一种新的策略，通过使用 DWPose 和 Segment Anything 模型来确保无意中移动被子的婴儿能够及时准确地再次覆盖，通过 EM*D 深度学习模型来预测被子状态的转变，并引入一种增强损失函数来提高网络对被子状态变化的敏感度。通过模拟和实际实验验证了该方法的有效性，在婴儿身上平铺和恢复被子。