Autonomous Quilt Spreading for Caregiving Robots

TL;DR

This paper presents a novel autonomous quilt spreading strategy for caregiving robots, combining advanced perception and deep learning to accurately detect infant and quilt states, ensuring prompt re-covering during sleep.

Contribution

It introduces a new two-step approach using human skeletal detection, instance segmentation, and deep learning to improve quilt spreading accuracy in caregiving robots.

Findings

Effective in simulation and real-world experiments

Accurately recognizes infant and quilt states during sleep

Improves quilt spreading reliability for caregiving robots

Abstract

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.