Autonomous Quilt Spreading for Caregiving Robots
This work addresses a specific caregiving task for infants, but it is incremental as it builds on prior research with minor enhancements.
The paper tackles the problem of infants displacing quilts during sleep by proposing a two-step method for autonomous quilt spreading using existing detection and segmentation models, achieving validated efficacy in both simulation and real-world experiments.
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.