  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.
